C05D9.3 Antibody

What is C05D9.3 and why is it important in neurodegenerative disease research?

C05D9.3 is a gene in the nematode Caenorhabditis elegans with human orthologs relevant to basement membrane function and interaction . The significance of C05D9.3 in neurodegenerative disease research stems from C. elegans' established role as a model organism for studying protein aggregation, which is a hallmark of many neurodegenerative conditions including Alzheimer's, Parkinson's, and polyglutamine diseases . C. elegans offers significant advantages for modeling these diseases due to its transparent body, well-characterized genome, and short lifespan, enabling rapid assessment of disease-modifying factors and potential therapeutic candidates . Antibodies against C05D9.3 provide crucial tools for investigating its protein product's function, localization, and potential role in proteostasis pathways that, when disrupted, contribute to neurodegeneration.

What are the recommended validation methods for C05D9.3 antibodies?

When validating C05D9.3 antibodies, researchers should implement a multi-step approach:

• Western blot validation: Confirm specificity by demonstrating a single band of appropriate molecular weight in wild-type worms and absence of this band in C05D9.3 deletion mutants.

• Immunohistochemistry controls: Perform parallel staining of wild-type and C05D9.3 knockout worms to confirm specificity of immunoreactive signals.

• Recombinant protein testing: Validate antibody binding to purified recombinant C05D9.3 protein.

• Cross-reactivity assessment: Test antibody against closely related proteins to ensure selective binding to C05D9.3.

• Peptide competition assay: Pre-incubate antibody with excess synthetic C05D9.3 peptide to confirm that this blocks immunoreactivity.

The validation should be conducted using standardized protocols with appropriate positive and negative controls to ensure reproducibility across different laboratories and experimental conditions .

How should researchers optimize fixation protocols when using C05D9.3 antibodies for immunostaining?

Optimization of fixation protocols for C05D9.3 immunostaining requires careful consideration of several factors:

• Fixative selection: Compare paraformaldehyde (2-4%) with methanol fixation to determine which better preserves C05D9.3 epitopes. Paraformaldehyde typically maintains cellular morphology while methanol may provide better antigen accessibility.

• Fixation duration: Test multiple timepoints (15 minutes to 24 hours) to find the optimal balance between tissue preservation and antibody penetration.

• Permeabilization method: After fixation, optimize membrane permeabilization using detergents like Triton X-100 (0.1-0.5%) or saponin (0.01-0.1%). The concentration and exposure time should be determined empirically for C05D9.3.

• Antigen retrieval: Evaluate whether heat-induced epitope retrieval (citrate buffer, pH 6.0) or enzymatic retrieval methods enhance C05D9.3 detection.

• Blocking conditions: Test different blocking solutions (normal serum, BSA, casein) at varying concentrations to minimize background staining.

The optimized protocol should be validated by comparing staining patterns in wild-type and C05D9.3 mutant worms to ensure specificity of the observed signals .

How can researchers address cross-reactivity concerns when using C05D9.3 antibodies in ortholog studies?

Addressing cross-reactivity concerns in C05D9.3 ortholog studies requires implementation of rigorous specificity controls:

• Sequential absorption approach: Pre-absorb antibodies against related proteins to remove cross-reactive antibodies. This involves incubating the C05D9.3 antibody with recombinant proteins of closely related family members, then recovering the non-bound fraction.

• Epitope mapping: Identify the specific epitopes recognized by the antibody and compare their conservation across species using bioinformatics tools. This helps predict potential cross-reactivity with human orthologs.

• Knockout validation across species: Test antibodies in tissues from relevant knockout models of multiple species to verify specificity across evolutionary boundaries.

• Competitive binding assays: Perform assays with increasing concentrations of putative cross-reactive proteins to quantify relative binding affinities.

• Orthogonal validation: Confirm antibody-based findings using orthogonal methods such as mass spectrometry or RNA interference to validate protein identification.

When studying human orthologs of C05D9.3, researchers should empirically determine cross-reactivity with each potential ortholog listed in the basement membrane ortholog database . This is particularly important when translating findings from C. elegans models to human neurodegenerative disease contexts.

What methodological approaches should be used when applying C05D9.3 antibodies in protein aggregation studies?

When applying C05D9.3 antibodies in protein aggregation studies, researchers should employ these methodological approaches:

• Sequential extraction protocol: Implement a multi-step extraction protocol using buffers of increasing solubilization strength (RIPA buffer → 2% SDS → 88% formic acid) to differentiate soluble, oligomeric, and highly insoluble aggregate forms of the protein.

• Density gradient ultracentrifugation: Separate protein aggregates by size and density to characterize different aggregation states of C05D9.3 and its interaction partners.

• Co-immunoprecipitation with aggregation-prone proteins: Use C05D9.3 antibodies to pull down protein complexes, followed by immunoblotting for known aggregation-prone proteins like polyglutamine expansion proteins to assess physical interactions .

• Fluorescence correlation spectroscopy: Combine with fluorescently-labeled C05D9.3 antibody fragments to measure diffusion rates of different protein species, providing insights into oligomerization states.

• Dual immunolabeling: Perform co-localization studies with markers of protein quality control compartments (JUNQ, IPOD, aggresome) to determine where C05D9.3 localizes during proteotoxic stress .

• Quantitative image analysis: Develop automated algorithms for quantifying colocalization of C05D9.3 with protein aggregates in immunofluorescence images.

These approaches should be integrated with genetic manipulation of C05D9.3 expression to establish causative relationships between this gene and aggregation phenotypes in models of neurodegenerative diseases.

How can researchers quantitatively assess C05D9.3 protein interactions with basement membrane components?

Quantitative assessment of C05D9.3 interactions with basement membrane components requires sophisticated biochemical and imaging approaches:

• Surface plasmon resonance (SPR): Immobilize purified basement membrane components on sensor chips and measure binding kinetics of C05D9.3, determining association (kon) and dissociation (koff) rate constants and calculating equilibrium dissociation constants (KD).

• Proximity ligation assay (PLA): Utilize paired antibodies (anti-C05D9.3 and anti-basement membrane component) with DNA-linked secondary antibodies to generate fluorescent signals only when proteins are in close proximity (<40 nm), allowing for quantitative in situ interaction analysis.

• Förster resonance energy transfer (FRET): Label C05D9.3 and basement membrane proteins with compatible fluorophore pairs to measure energy transfer efficiency, which correlates with molecular proximity.

• Co-sedimentation assays: Mix purified C05D9.3 with basement membrane components and analyze co-precipitation using differential centrifugation followed by immunoblotting with specific antibodies.

• Isothermal titration calorimetry (ITC): Measure thermodynamic parameters of C05D9.3 binding to basement membrane components to determine binding stoichiometry, enthalpy, and entropy.

Data from these complementary approaches should be integrated to build comprehensive interaction models. Researchers should also consider the potential impact of post-translational modifications on these interactions, as these may be regulated differently under neurodegenerative disease conditions .

What controls are essential when using C05D9.3 antibodies in C. elegans neurodegenerative disease models?

When implementing C05D9.3 antibody-based studies in C. elegans neurodegenerative disease models, researchers must include these essential controls:

• Genetic controls:

  • Wild-type C. elegans (N2 strain)

  • C05D9.3 deletion mutants (negative control)

  • C05D9.3 overexpression strains (positive control)

  • Disease model strains without manipulation of C05D9.3 (baseline comparator)

• Antibody controls:

  • Secondary antibody-only condition to assess non-specific binding

  • Pre-immune serum from the same host species

  • Isotype control antibodies at matching concentrations

  • Peptide competition controls using immunizing peptide

• Technical controls:

  • Age-synchronized worm populations to control for developmental effects

  • Temperature-controlled experiments (typically 20°C) with precise monitoring

  • FUDR-treated plates to prevent progeny production during longitudinal studies

  • Vehicle controls for any drug treatments

• Statistical controls:

  • Randomization of sample processing order

  • Blinded analysis of images and phenotypes

  • Technical replicates (minimum 3) and biological replicates (minimum 3)

  • Power analysis to determine appropriate sample size

These controls should be systematically implemented across experiments to ensure valid and reproducible results when investigating C05D9.3's role in protein homeostasis and neurodegenerative pathways .

What is the recommended protocol for combining C05D9.3 antibody staining with automated behavioral analysis in C. elegans?

The integration of C05D9.3 antibody staining with automated behavioral analysis requires careful experimental design:

• Sequential analysis approach:

  • First perform automated behavioral tracking on living worms

  • Record individual worm identifiers and behavioral data

  • Fix and stain the same populations for C05D9.3 immunohistochemistry

  • Correlate behavioral phenotypes with C05D9.3 expression or localization patterns

• Sample preparation protocol:

  • Culture worms according to standardized protocols on Rich-NGM plates

  • Perform behavioral analysis using WF-NTP or similar platforms at L4 or young adult stage

  • Fix worms immediately after behavioral recording using optimized fixation protocol

  • Process for immunostaining with validated C05D9.3 antibodies

• Data integration method:

  • Establish unambiguous sample tracking system throughout the workflow

  • Use computational tools to match behavioral metrics with immunostaining intensity

  • Perform regression analysis between quantified C05D9.3 signals and behavioral parameters

  • Apply machine learning algorithms to identify patterns linking protein expression to behavior

• Validation steps:

  • Confirm that fixation and staining procedures do not create artifacts that confound data interpretation

  • Verify that the time delay between behavioral assessment and fixation is minimized and consistent

  • Compare results with transgenic C. elegans expressing fluorescently tagged C05D9.3

This integrated approach enables direct correlation between molecular phenotypes and functional outcomes, providing mechanistic insights into how C05D9.3-related pathways influence behavior in neurodegenerative disease models .

How should researchers design time-course experiments to study C05D9.3 dynamics during protein aggregation?

Design of time-course experiments to study C05D9.3 dynamics during protein aggregation requires careful consideration of temporal, technical, and analytical factors:

• Developmental staging protocol:

  • Synchronize worm populations using bleach synchronization protocol

  • Precisely control temperature conditions throughout development

  • Transfer L1 larvae (3,000 worms per plate) to Rich-NGM plates for consistent development

  • Begin measurements at defined developmental stages (L4 recommended as baseline)

• Sampling intervals:

  • Early aggregation phase: Sample every 2-4 hours

  • Intermediate phase: Sample every 6-12 hours

  • Late/stable phase: Sample every 24 hours

  • Continue until death or up to 15 days to capture full aggregation progression

• Parallel analysis methods:

  • Immunoblotting: Assess C05D9.3 protein levels and solubility changes

  • Immunohistochemistry: Track subcellular localization and colocalization with aggregates

  • qRT-PCR: Monitor C05D9.3 mRNA expression changes

  • Proteomics: Identify changing interaction partners throughout aggregation process

• Quantification approach:

  • Implement automated image analysis algorithms to quantify aggregation parameters

  • Measure aggregate count, size, density, and colocalization with C05D9.3

  • Plot correlation between C05D9.3 metrics and aggregation parameters over time

  • Apply mathematical modeling to determine rate constants and inflection points

This experimental design allows researchers to establish whether C05D9.3 acts as an early biomarker, active participant, or downstream consequence in the protein aggregation cascade .

What are the common pitfalls when using C05D9.3 antibodies and how can they be addressed?

Researchers frequently encounter several technical challenges when working with C05D9.3 antibodies. These pitfalls and their methodological solutions include:

• High background staining:

  • Problem: Non-specific binding to C. elegans tissues

  • Solution: Optimize blocking with 5-10% normal serum from secondary antibody species plus 1-3% BSA; pre-absorb antibody against acetone powder of C05D9.3 knockout worms; include 0.1% Tween-20 in wash buffers; extend washing steps to 6 × 10 minutes

• Inconsistent epitope accessibility:

  • Problem: Variable staining patterns between experiments

  • Solution: Standardize fixation timing precisely; implement heat-mediated antigen retrieval (10 mM sodium citrate, pH 6.0, 95°C for 10 minutes); optimize permeabilization with titrated detergent concentrations

• Cross-reactivity with related proteins:

  • Problem: Antibody recognizes proteins beyond C05D9.3

  • Solution: Validate with multiple antibodies targeting different epitopes; confirm specificity using C05D9.3 null mutants; perform peptide competition assays with increasing concentrations of immunizing peptide

• Batch-to-batch variability:

  • Problem: Different antibody lots produce inconsistent results

  • Solution: Request detailed QC data from vendors; maintain reference samples for standardization; pool antibody lots when possible; validate each new lot against established standards

• Protein extraction inefficiency:

  • Problem: Incomplete solubilization of C05D9.3 from tissues

  • Solution: Optimize extraction buffers (test RIPA, urea-based, and SDS-based buffers); incorporate sonication steps; use sequential extraction protocols to recover membrane-associated fractions

Each laboratory should develop a standardized troubleshooting decision tree to systematically address these issues when they arise, ensuring experimental reproducibility and reliable data interpretation .

How can researchers resolve data inconsistencies between C05D9.3 antibody-based detection and other methodologies?

When faced with discrepancies between C05D9.3 antibody-based results and other methodological approaches, researchers should implement this systematic resolution framework:

• Methodological cross-validation strategy:

  • Compare C05D9.3 protein levels detected by antibodies with mRNA expression via qRT-PCR

  • Validate antibody findings with orthogonal techniques (mass spectrometry, CRISPR tagging)

  • Test multiple antibodies targeting different C05D9.3 epitopes

  • Correlate immunohistochemistry results with live imaging of fluorescently tagged C05D9.3

• Technical parameter assessment:

  • Evaluate antibody specificity via immunoprecipitation followed by mass spectrometry

  • Test sensitivity limits of each method using purified C05D9.3 protein standards

  • Determine whether post-translational modifications affect antibody recognition

  • Assess whether protein conformation or aggregation state impacts detection efficiency

• Biological variable consideration:

  • Control for developmental stage variation using precisely synchronized populations

  • Account for potential circadian or stress-induced expression changes

  • Consider whether C05D9.3 undergoes regulated proteolytic processing

  • Evaluate whether disease models affect transcript-to-protein correlation

• Integrated data analysis approach:

  • Implement multivariate statistical methods to identify factors driving inconsistencies

  • Develop computational models that integrate data from multiple methodologies

  • Establish confidence intervals for measurements across different techniques

  • Consider whether discrepancies reveal novel biological insights rather than technical errors

This structured approach not only resolves inconsistencies but may uncover important regulatory mechanisms affecting C05D9.3 expression, processing, or function that would be missed by relying on a single methodology .

What statistical approaches are recommended for analyzing quantitative C05D9.3 antibody data in aging and neurodegeneration studies?

Analysis of quantitative C05D9.3 antibody data in aging and neurodegeneration studies requires sophisticated statistical approaches:

• Longitudinal data analysis methods:

  • Linear mixed effects models to account for repeated measures across time points

  • Survival analysis (Kaplan-Meier with log-rank tests) to correlate C05D9.3 expression with lifespan

  • Time-to-event analysis for aggregation onset or behavioral phenotypes

  • Area-under-the-curve calculations to quantify cumulative C05D9.3 expression patterns

• Comparing multiple experimental groups:

  • ANOVA with appropriate post-hoc tests (Tukey's HSD or Dunnett's test) for normally distributed data

  • Non-parametric alternatives (Kruskal-Wallis with Dunn's post-hoc test) for non-normal distributions

  • Two-way ANOVA to assess interaction effects between genotype and treatment

  • ANCOVA to control for covariates like developmental timing or protein expression levels

• Correlation and regression approaches:

  • Pearson or Spearman correlation to assess relationships between C05D9.3 levels and phenotypic measures

  • Multiple regression to identify predictors of aggregation or neurodegeneration

  • Principal component analysis to reduce dimensionality of complex datasets

  • Hierarchical clustering to identify patterns in C05D9.3 expression across experimental conditions

• Advanced statistical considerations:

  • Power analysis to determine appropriate sample sizes (typically n≥50 worms per condition)

  • Bootstrapping for robust confidence interval estimation

  • Permutation tests for hypothesis testing with non-standard distributions

  • Bayesian modeling to incorporate prior knowledge about C05D9.3 function

These statistical approaches should be determined during experimental design phase rather than post-hoc, and should be accompanied by rigorous reporting of all statistical parameters, including effect sizes and confidence intervals .

How does C05D9.3 expression change in different C. elegans models of neurodegenerative diseases?

C05D9.3 expression exhibits distinct patterns across various C. elegans neurodegenerative disease models, with important implications for understanding conserved pathological mechanisms:

These expression patterns suggest that C05D9.3 responds dynamically to proteotoxic stress across different disease models, potentially serving as either a protective factor or disease modifier depending on the specific proteinopathy context . Importantly, the observed sequestration of MOAG-2/LIR-3 (a C05D9.3-interacting protein) by polyglutamine expansion proteins suggests functional alterations beyond mere expression changes .

What methodological approaches can determine whether C05D9.3 is causative or consequential in protein aggregation?

Determining whether C05D9.3 plays a causative or consequential role in protein aggregation requires a comprehensive experimental approach:

• Temporal analysis methods:

  • Time-resolved immunofluorescence to determine whether C05D9.3 changes precede aggregation

  • Inducible expression systems to control C05D9.3 expression at defined time points

  • Photoconvertible tagged C05D9.3 to track protein movement before and during aggregation

  • Time-lapse microscopy correlating C05D9.3 dynamics with aggregate formation

• Genetic manipulation strategies:

  • CRISPR/Cas9-mediated C05D9.3 knockout in disease models to assess aggregation outcomes

  • RNAi knockdown with varying efficiency to establish dose-dependent effects

  • Tissue-specific or temporally-controlled C05D9.3 expression to isolate its function

  • Point mutations in functional domains to identify critical regions for aggregation effects

• Biochemical interaction studies:

  • In vitro aggregation assays with recombinant proteins to test direct effects

  • Cross-linking followed by immunoprecipitation to capture transient interactions

  • Filter trap assays to quantify insoluble protein formation with/without C05D9.3

  • Cell-free protein synthesis systems to reconstruct minimal aggregation machinery

• Rescue experiments:

  • Express human orthologs in C05D9.3 mutant backgrounds to test functional conservation

  • Structure-function analysis with domain deletion constructs

  • Test whether C05D9.3 overexpression can accelerate aggregation in pre-symptomatic models

  • Pharmacological manipulation of C05D9.3-related pathways

Integration of these approaches with appropriate controls can establish causation rather than merely correlation, distinguishing whether C05D9.3 is an initiator, accelerator, or consequence of the aggregation process in neurodegenerative disease contexts .

How can C05D9.3 antibodies be applied in screening for modifiers of proteotoxicity?

C05D9.3 antibodies can be strategically employed in high-throughput screening approaches to identify modifiers of proteotoxicity:

• Automated immunofluorescence screening platform:

  • Establish baseline C05D9.3 staining patterns in disease models

  • Use automated microscopy to image thousands of treated worms

  • Develop machine learning algorithms to classify C05D9.3 localization patterns

  • Correlate changes in C05D9.3 distribution with aggregation phenotypes

• Targeted RNAi screening methodology:

  • Screen candidate genes (100-1,000) for effects on C05D9.3 localization

  • Use automated behavioral analysis to correlate molecular changes with functional outcomes

  • Implement hierarchical screening design starting with pathway-focused gene sets

  • Validate hits with secondary assays including lifespan and aggregation quantification

• Small molecule screening approach:

  • Screen compound libraries (1,000-100,000 compounds) for modulators of C05D9.3 expression

  • Utilize C05D9.3 antibodies in high-content imaging to assess subcellular localization changes

  • Implement ELISA-based detection of C05D9.3 levels in whole-worm lysates

  • Correlate compound effects on C05D9.3 with aggregation and toxicity phenotypes

• Multiparametric phenotypic analysis:

  • Combine C05D9.3 antibody staining with additional markers of proteostasis

  • Develop phenotypic fingerprints based on multiple immunostaining patterns

  • Cluster compounds or genetic modifiers based on similarity of response profiles

  • Identify novel pathway connections based on similar phenotypic signatures

This systematic screening approach enables identification of both genetic and pharmacological modifiers of proteotoxicity that act through C05D9.3-dependent mechanisms, potentially revealing therapeutic targets for intervention in neurodegenerative diseases .

How might C05D9.3 antibodies be adapted for super-resolution microscopy in neurodegenerative disease models?

Adaptation of C05D9.3 antibodies for super-resolution microscopy requires specialized methodological approaches:

• Antibody fragment generation strategy:

  • Engineer smaller antibody formats (Fab fragments, nanobodies) against C05D9.3

  • Verify epitope recognition is maintained in smaller formats

  • Optimize labeling density to achieve Nyquist sampling criteria

  • Test multiple fluorophore conjugation methods to identify optimal signal-to-noise ratio

• Sample preparation optimization:

  • Develop C. elegans-specific clearing protocols compatible with immunolabeling

  • Evaluate expansion microscopy approaches to physically magnify specimens

  • Optimize mounting media to minimize photobleaching and maximize photon yield

  • Implement drift correction strategies for long acquisition times

• Multi-color imaging approach:

  • Combine C05D9.3 antibodies with markers of aggregates and subcellular compartments

  • Select fluorophores with appropriate photophysical properties (photoswitching, photoactivation)

  • Establish spectral unmixing protocols to separate closely overlapping signals

  • Implement sequential imaging strategies for crowded epitopes

• Quantitative analysis methods:

  • Develop algorithms for nanoscale colocalization analysis

  • Implement nearest neighbor analysis to quantify molecular clustering

  • Apply Ripley's K-function and related spatial statistics to characterize distribution patterns

  • Utilize machine learning for automated feature extraction from super-resolution datasets

These methodological adaptations would enable visualization of C05D9.3 distribution at nanoscale resolution (10-20 nm), potentially revealing previously undetectable interactions with aggregation-prone proteins and providing insights into the spatial organization of proteostasis factors in neurodegenerative disease contexts .

What strategies can integrate C05D9.3 antibody-based findings with proteomic and transcriptomic data?

Integration of C05D9.3 antibody-based findings with multi-omic data requires sophisticated methodological strategies:

• Sequential multi-omic workflow design:

  • Split samples for parallel processing through antibody-based, proteomic and transcriptomic pipelines

  • Implement strict sample tracking and metadata collection

  • Establish computational pipelines to align and integrate heterogeneous data types

  • Utilize reference standards across all three platforms for cross-calibration

• Single-cell analysis approaches:

  • Adapt C05D9.3 antibodies for compatibility with single-cell mass cytometry (CyTOF)

  • Develop protocols for sequential immunofluorescence and single-cell RNA-seq

  • Implement spatial transcriptomics in conjunction with C05D9.3 immunohistochemistry

  • Utilize computational methods to integrate single-cell datasets across modalities

• Network analysis methodology:

  • Construct protein-protein interaction networks centered on C05D9.3

  • Overlay transcriptional response data from neurodegenerative disease models

  • Apply weighted gene correlation network analysis (WGCNA) to identify modules

  • Implement Bayesian network approaches to infer causal relationships

• Validation strategy for integration findings:

  • Prioritize hub genes/proteins from integrated networks for functional validation

  • Implement CRISPR screening of predicted C05D9.3 interactors

  • Test computational predictions with targeted co-immunoprecipitation experiments

  • Validate transcription factor predictions with chromatin immunoprecipitation

This integrated approach would enable researchers to position C05D9.3 within larger regulatory networks and identify key pathways through which it influences proteostasis and neurodegeneration, potentially revealing new therapeutic targets and biomarkers .

How can C05D9.3 antibody research inform translational approaches for human neurodegenerative diseases?

Translating C05D9.3 antibody research findings to human neurodegenerative diseases requires methodological bridges between model systems and clinical applications:

• Cross-species validation pathway:

  • Identify human orthologs of C05D9.3 using comprehensive ortholog databases

  • Generate and validate antibodies against human orthologs with identical protocols

  • Compare expression patterns in C. elegans models and human postmortem tissues

  • Establish functional conservation through rescue experiments with human genes

• Biomarker development methodology:

  • Evaluate whether human C05D9.3 orthologs show altered expression in patient biofluids

  • Develop sensitive ELISA or other immunoassays for detection in CSF or plasma

  • Correlate levels with disease progression in longitudinal patient cohorts

  • Assess potential as companion biomarkers for clinical trials

• Therapeutic target validation approach:

  • Screen for small molecules that normalize C05D9.3 ortholog function in human cells

  • Develop assays to monitor target engagement in patient-derived models

  • Test compounds identified in C. elegans screens for efficacy in mammalian models

  • Establish translational biomarkers based on C05D9.3 pathway modulation

• Patient stratification strategy:

  • Analyze C05D9.3 ortholog expression patterns across patient subgroups

  • Develop immunohistochemistry protocols for postmortem tissue classification

  • Correlate genetic variants in C05D9.3 orthologs with disease subtypes

  • Establish whether C05D9.3 pathway status predicts response to experimental therapies

This translational framework would maximize the clinical impact of basic research findings on C05D9.3, potentially leading to novel diagnostic, prognostic, or therapeutic approaches for human neurodegenerative diseases .