KEGG: spo:SPCC417.06c
STRING: 4896.SPCC417.06c.1
VPS35 (Vacuolar protein sorting-associated protein 35) functions as a subunit of the retromer complex, which plays a crucial role in the endosomal protein sorting pathway. Its significance in Parkinson's Disease research stems from the discovery that the D620N mutation in the VPS35 gene is linked to type 17 Parkinson's Disease (PARK17) . While this genetic association has been established, the exact molecular mechanisms through which mutant VPS35 contributes to neurodegeneration remain incompletely understood, making it an important target for investigation using validated antibodies .
A high-performing VPS35 antibody is defined differently based on the specific application:
Western Blot: A high-performing antibody specifically detects the target protein in Wild-type (WT) lysates but shows no signal in knockout (KO) lysates .
Immunoprecipitation: Success is defined by the ability to immunocapture at least 10% of the starting material containing the target protein .
Immunofluorescence: A successful antibody generates a fluorescent signal that is at least 1.5-fold higher in WT cells compared to KO cells .
These standardized definitions enable researchers to objectively evaluate antibody performance across different experimental techniques.
Based on extensive transcriptomic analysis using the DepMap database, HAP1 cells have been identified as expressing VPS35 at levels above the average range observed in cancer cells (>2.5 log2 TPM+1) . The following cell lines are specifically recommended:
| Institution | Catalog number | RRID (Cellosaurus) | Cell line | Genotype |
|---|---|---|---|---|
| Horizon Discovery | C631 | CVCL_Y019 | HAP1 | WT |
| Horizon Discovery | HZGHC000863c012 | CVCL_TX57 | HAP1 | VPS35 KO |
These paired cell lines (WT and KO) provide the optimal experimental system for antibody validation as they allow direct comparison of antibody specificity in cells with and without the target protein .
For immunofluorescence validation of VPS35 antibodies, a mosaic strategy is recommended, where WT and KO cells are plated together in the same well and imaged in the same field of view . The detailed methodology includes:
Cell preparation: Label HAP1 WT and VPS35 KO cells with green and far-red fluorescence dyes respectively (Thermo Fisher Scientific cat. numbers C2925 and C34565).
Plating: Plate labeled cells in 96-well glass plates (Perkin Elmer, cat. number 6055300) as a mosaic pattern.
Incubation: Incubate for 24 hours at 37°C with 5% CO₂.
Fixation: Fix cells with paraformaldehyde (PFA) in PBS for 15 minutes at room temperature, followed by three PBS washes.
Permeabilization: Treat with 0.1% Triton X-100 in PBS for 10 minutes at room temperature.
Blocking: Block with PBS containing 5% BSA, 5% goat serum, and 0.01% Triton X-100 for 30 minutes.
Primary antibody: Incubate cells overnight at 4°C with primary VPS35 antibodies in IF buffer (PBS, 5% BSA, 0.01% Triton X-100).
Secondary antibody: After washing, incubate with Alexa Fluor 555-conjugated secondary antibodies (1.0 μg/mL) in IF buffer for 1 hour at room temperature with DAPI .
This mosaic approach minimizes staining, imaging, and image analysis bias by allowing direct comparison of antibody performance in cells with and without the target protein.
When interpreting Western Blot results for VPS35 antibody validation, researchers should:
Compare WT vs. KO lysates: Run protein extracts from WT and VPS35 KO cells side-by-side and probe with the antibody in question.
Assess specificity: A specific antibody should show a clear band at the expected molecular weight (~92 kDa for VPS35) in WT lysates and no corresponding band in KO lysates.
Evaluate background: Examine non-specific bands across both samples; minimal background indicates higher antibody quality.
Consider signal intensity: Strong signal-to-noise ratio in WT samples suggests better antibody performance .
Importantly, rather than making subjective assessments, researchers should document the presence/absence of bands at the expected molecular weight and the ratio of specific to non-specific signals when comparing different antibodies.
Advanced computational approaches can significantly improve VPS35 antibody specificity through:
Binding mode identification: Computational models can identify distinct binding modes associated with particular ligands, enabling the design of antibodies with custom specificity profiles beyond those observed in experimental selections .
Biophysics-informed modeling: Models trained on experimentally selected antibodies can associate potential ligands with distinct binding modes, facilitating the prediction and generation of specific variants .
Optimization algorithms: By jointly minimizing or maximizing energy functions associated with desired or undesired ligands respectively, researchers can design antibodies with either cross-specific (binding to multiple targets) or highly specific (binding to a single target) profiles .
This computational approach is particularly valuable for VPS35 research as it could help design antibodies that specifically recognize mutant forms (e.g., D620N) while excluding wild-type VPS35, or antibodies that can distinguish between different conformational states of the protein.
Distinguishing between different conformational states of VPS35 (particularly relevant for understanding disease mechanisms) requires sophisticated antibody development approaches:
Conformation-specific epitope selection: Identify epitopes that are exposed or hidden in different conformational states of VPS35, particularly focusing on regions that might be affected by the D620N mutation.
Phage display with negative selection: Implement phage display experiments with alternating positive selection (against one conformational state) and negative selection (against other conformational states) to identify antibodies that specifically recognize particular protein conformations .
Computational disentanglement of binding modes: Apply biophysics-informed models to identify and separate multiple binding modes associated with specific conformational states of VPS35 .
Validation in cellular contexts: Test conformational state-specific antibodies in cellular models where VPS35 is known to adopt different conformations, such as under different cellular stresses or in the presence of retromer complex partners.
This approach allows researchers to use antibodies as tools to monitor VPS35 conformational changes that may occur during cellular trafficking or in disease states.
When characterizing VPS35 antibodies through multiple selection rounds, researchers should implement these strategies to address potential amplification bias:
Sequence before and after amplification: Collect sequencing data immediately before and after amplification steps to identify and quantify any amplification bias .
Compare nucleotide vs. amino acid level selection: Analyze selection data at both nucleotide and amino acid levels to confirm that selection is primarily driven by protein-level interactions rather than codon bias .
Implement bias correction algorithms: Apply computational corrections to account for identified biases in the amplification process.
Use control selections: Include selections against unrelated targets to establish baseline amplification biases in the experimental system.
Research has shown that no significant amplification bias was present in well-designed experiments, and selection modes primarily arise from ligand binding rather than nucleotide-level effects .
While standardized protocols are essential for objective antibody evaluation, researchers should be aware of several limitations:
Cell line specificity: Results obtained in HAP1 cells may not translate directly to other cell types that express different levels of VPS35 or have different post-translational modifications .
Protocol constraints: Universal protocols may not capture antibody performance under all possible experimental conditions; modifications might be necessary for specific research questions .
Interpretation context: Antibody performance should be interpreted within the specific experimental setup and cell line used, as stated by the YCharOS initiative .
Absence of functional assays: Standard validation protocols focus on target recognition but may not assess how antibody binding affects protein function, which could be relevant for certain applications.
Advanced antibody engineering approaches for studying VPS35-retromer complex dynamics include:
Proximity-dependent labeling: Engineer VPS35 antibodies conjugated with enzymes like BioID or APEX2 to identify proteins that transiently interact with VPS35 in the retromer complex under different conditions.
Split reporter systems: Develop antibody fragments that recognize different components of the retromer complex, each conjugated with half of a reporter protein (like split GFP) to visualize complex assembly in real-time.
Intrabodies: Design antibodies that can function inside living cells to track VPS35 dynamics without fixation artifacts.
Conditional binding: Create antibodies that bind VPS35 only under specific cellular conditions (pH, redox state) to monitor how these conditions affect retromer assembly.
These approaches would significantly advance our understanding of how the D620N mutation in VPS35 affects retromer complex formation and function in Parkinson's disease.
Innovative combinations of VPS35 antibodies with other research tools offer promising approaches to elucidate PARK17 pathogenesis:
Antibodies with live-cell imaging: Combine VPS35 antibody fragments with emerging live-cell super-resolution microscopy techniques to visualize retromer trafficking in real-time.
Antibody-guided CRISPR screens: Use VPS35 antibodies to isolate retromer complexes and identify synthetic lethal interactions through targeted CRISPR screens in cells expressing WT versus D620N VPS35.
Antibody-based proteomics: Employ VPS35 antibodies in proximity labeling approaches to map the changing interactome of mutant versus wild-type VPS35 under different cellular stresses.
Single-cell analysis with antibody detection: Combine VPS35 antibody-based detection with single-cell transcriptomics to correlate VPS35 protein levels/localization with gene expression patterns in neurons.
These integrative approaches could provide multi-dimensional insights into how VPS35 mutations lead to neurodegeneration and identify potential therapeutic intervention points.