Validation experiment designed to validate causal mechanisms targeting CBS in cell_line. Primary outcome: Cell-type-specific signatures distinguishing CBS from PSP in single-nucleus multi-omics
Description
CBS vs PSP Phenotype Determinants — Single-Nucleus Multi-Omics Study
Background and Rationale
Corticobasal syndrome (CBS) and progressive supranuclear palsy (PSP) represent distinct clinical phenotypes of 4-repeat tauopathy, yet the molecular determinants underlying their divergent presentations remain poorly understood. Both conditions share similar tau isoform pathology but exhibit markedly different anatomical distribution patterns, clinical presentations, and disease progression rates. This fundamental paradox suggests that factors beyond tau isoform composition - including tau conformational strains, cell-type-specific vulnerability patterns, and regional epigenetic landscapes - drive phenotypic divergence. This validation study employs an integrated single-nucleus multi-omics approach to systematically dissect the molecular architecture underlying CBS versus PSP phenotypes across four strategically selected brain regions that show differential vulnerability patterns between these conditions....
CBS vs PSP Phenotype Determinants — Single-Nucleus Multi-Omics Study
Background and Rationale
Corticobasal syndrome (CBS) and progressive supranuclear palsy (PSP) represent distinct clinical phenotypes of 4-repeat tauopathy, yet the molecular determinants underlying their divergent presentations remain poorly understood. Both conditions share similar tau isoform pathology but exhibit markedly different anatomical distribution patterns, clinical presentations, and disease progression rates. This fundamental paradox suggests that factors beyond tau isoform composition - including tau conformational strains, cell-type-specific vulnerability patterns, and regional epigenetic landscapes - drive phenotypic divergence. This validation study employs an integrated single-nucleus multi-omics approach to systematically dissect the molecular architecture underlying CBS versus PSP phenotypes across four strategically selected brain regions that show differential vulnerability patterns between these conditions. The motor cortex and frontal cortex represent cortical regions with CBS-predominant pathology, while the subthalamic nucleus shows PSP-specific vulnerability, and the dentate nucleus serves as a comparative cerebellar region. Our experimental design combines single-nucleus RNA sequencing to capture cell-type-specific transcriptional states, single-nucleus ATAC sequencing to profile chromatin accessibility landscapes, and high-resolution spatial transcriptomics to preserve anatomical context of molecular signatures. Critically, we integrate these genomic approaches with direct biochemical characterization of tau conformational strains through limited proteolysis-mass spectrometry and cryo-electron microscopy structural analysis. This multi-dimensional approach will reveal whether CBS and PSP represent distinct tau strains propagating through shared cellular networks, or identical tau strains encountering divergent cellular environments that shape disease phenotype. The innovation lies in linking single-cell molecular signatures directly to tau structural conformations within the same tissue samples, enabling unprecedented mechanistic insight into tauopathy phenotype determination. Results will inform development of strain-specific biomarkers for early differential diagnosis and guide precision therapeutic approaches targeting the specific molecular drivers of each phenotype rather than generic tau pathology.
This experiment directly tests predictions arising from the following hypotheses:
TREM2-mediated microglial tau clearance enhancement
LRP1-Dependent Tau Uptake Disruption
HSP90-Tau Disaggregation Complex Enhancement
Synaptic Vesicle Tau Capture Inhibition
Tau-Independent Microtubule Stabilization via MAP6 Enhancement
Experimental Protocol
Phase 1 (Months 1-3): Tissue procurement and processing. Obtain fresh-frozen post-mortem brain tissue from CBS (n=15), PSP (n=15), and age-matched controls (n=15) within 12-hour post-mortem interval. Dissect motor cortex, subthalamic nucleus, dentate nucleus, and frontal cortex regions using standardized anatomical landmarks. Flash-freeze samples in liquid nitrogen and store at -80°C. Phase 2 (Months 4-8): Single-nucleus isolation and library preparation. Generate single-nucleus suspensions using optimized sucrose gradient protocol with DAPI staining for nuclei verification. Prepare single-nucleus RNA-seq libraries using 10x Genomics Chromium platform targeting 10,000 nuclei per sample. Generate single-nucleus ATAC-seq libraries using 10x Multiome workflow with 5,000 nuclei per sample. Perform quality control including nuclei viability assessment and library complexity validation. Phase 3 (Months 6-10): Spatial transcriptomics and tau analysis. Process adjacent 10μm tissue sections for MERFISH spatial transcriptomics targeting 300 genes including tau, glial markers, and neuroinflammatory panels. Extract tau fibrils using sarkosyl extraction protocol followed by limited proteolysis using trypsin and pepsin digestion. Analyze digestion patterns via LC-MS/MS and perform cryo-EM structural analysis of isolated filaments. Phase 4 (Months 11-15): Sequencing and data generation. Sequence libraries on Illumina NovaSeq targeting 50,000 reads per nucleus for RNA-seq and 25,000 reads per nucleus for ATAC-seq. Process MERFISH imaging data using standard analysis pipelines. Phase 5 (Months 16-18): Integrated analysis combining transcriptomic, epigenetic, spatial, and structural datasets using machine learning approaches to identify phenotype-determining signatures.
Expected Outcomes
1. Identification of 3-5 distinct astrocyte transcriptional states with CBS-specific activation signatures showing >2-fold enrichment in inflammatory and complement pathways compared to PSP samples (FDR < 0.01).
2. Discovery of oligodendrocyte-specific chromatin accessibility patterns with 200-500 differentially accessible peaks between CBS and PSP, particularly in myelination and stress response gene regulatory regions (FDR < 0.05).
3. Detection of regional heterogeneity with motor cortex showing 80% CBS-associated signatures while subthalamic nucleus exhibits 85% PSP-specific molecular patterns across all cell types.
4. Identification of 2-3 distinct tau conformational strains with CBS samples showing increased protease resistance at specific cleavage sites and distinct cryo-EM fibril morphologies compared to PSP.
5. Spatial transcriptomics revealing CBS-associated microglial clustering within 50μm of tau-positive neurons with upregulated phagocytic gene expression (>3-fold vs controls).
6. Machine learning classifier achieving >90% accuracy in distinguishing CBS from PSP samples based on integrated multi-omics signatures from astrocytes and oligodendrocytes.
Success Criteria
• Identify minimum 3 cell-type-specific gene expression modules (>100 genes each) that significantly distinguish CBS from PSP with FDR < 0.05 and effect size > 1.5
• Detect statistically significant correlation (r > 0.6, p < 0.01) between tau structural conformations and cell-type-specific transcriptional signatures within the same brain regions
• Achieve successful single-nucleus library preparation from >80% of tissue samples with >5,000 high-quality nuclei recovered per sample and <15% mitochondrial gene contamination
• Demonstrate reproducible regional molecular signatures with >70% consistency across biological replicates within each disease group and brain region
• Validate spatial organization patterns showing significant association between tau pathology locations and glial activation signatures within 100μm proximity zones
• Generate integrated multi-omics classifier with cross-validated accuracy >85% for CBS vs PSP discrimination and identification of top 50 predictive molecular features
TARGET GENE
CBS
MODEL SYSTEM
cell_line
ESTIMATED COST
$160,000
TIMELINE
7 months
PATHWAY
N/A
SOURCE
wiki
PRIMARY OUTCOME
Cell-type-specific signatures distinguishing CBS from PSP in single-nucleus multi-omics
Phase 1 (Months 1-3): Tissue procurement and processing. Obtain fresh-frozen post-mortem brain tissue from CBS (n=15), PSP (n=15), and age-matched controls (n=15) within 12-hour post-mortem interval. Dissect motor cortex, subthalamic nucleus, dentate nucleus, and frontal cortex regions using standardized anatomical landmarks. Flash-freeze samples in liquid nitrogen and store at -80°C. Phase 2 (Months 4-8): Single-nucleus isolation and library preparation. Generate single-nucleus suspensions using optimized sucrose gradient protocol with DAPI staining for nuclei verification. Prepare single-nucleus RNA-seq libraries using 10x Genomics Chromium platform targeting 10,000 nuclei per sample. Generate single-nucleus ATAC-seq libraries using 10x Multiome workflow with 5,000 nuclei per sample.
...
Phase 1 (Months 1-3): Tissue procurement and processing. Obtain fresh-frozen post-mortem brain tissue from CBS (n=15), PSP (n=15), and age-matched controls (n=15) within 12-hour post-mortem interval. Dissect motor cortex, subthalamic nucleus, dentate nucleus, and frontal cortex regions using standardized anatomical landmarks. Flash-freeze samples in liquid nitrogen and store at -80°C. Phase 2 (Months 4-8): Single-nucleus isolation and library preparation. Generate single-nucleus suspensions using optimized sucrose gradient protocol with DAPI staining for nuclei verification. Prepare single-nucleus RNA-seq libraries using 10x Genomics Chromium platform targeting 10,000 nuclei per sample. Generate single-nucleus ATAC-seq libraries using 10x Multiome workflow with 5,000 nuclei per sample. Perform quality control including nuclei viability assessment and library complexity validation. Phase 3 (Months 6-10): Spatial transcriptomics and tau analysis. Process adjacent 10μm tissue sections for MERFISH spatial transcriptomics targeting 300 genes including tau, glial markers, and neuroinflammatory panels. Extract tau fibrils using sarkosyl extraction protocol followed by limited proteolysis using trypsin and pepsin digestion. Analyze digestion patterns via LC-MS/MS and perform cryo-EM structural analysis of isolated filaments. Phase 4 (Months 11-15): Sequencing and data generation. Sequence libraries on Illumina NovaSeq targeting 50,000 reads per nucleus for RNA-seq and 25,000 reads per nucleus for ATAC-seq. Process MERFISH imaging data using standard analysis pipelines. Phase 5 (Months 16-18): Integrated analysis combining transcriptomic, epigenetic, spatial, and structural datasets using machine learning approaches to identify phenotype-determining signatures.
Expected Outcomes
1. Identification of 3-5 distinct astrocyte transcriptional states with CBS-specific activation signatures showing >2-fold enrichment in inflammatory and complement pathways compared to PSP samples (FDR < 0.01).
2. Discovery of oligodendrocyte-specific chromatin accessibility patterns with 200-500 differentially accessible peaks between CBS and PSP, particularly in myelination and stress response gene regulatory regions (FDR < 0.05).
3.
...
1. Identification of 3-5 distinct astrocyte transcriptional states with CBS-specific activation signatures showing >2-fold enrichment in inflammatory and complement pathways compared to PSP samples (FDR < 0.01).
2. Discovery of oligodendrocyte-specific chromatin accessibility patterns with 200-500 differentially accessible peaks between CBS and PSP, particularly in myelination and stress response gene regulatory regions (FDR < 0.05).
3. Detection of regional heterogeneity with motor cortex showing 80% CBS-associated signatures while subthalamic nucleus exhibits 85% PSP-specific molecular patterns across all cell types.
4. Identification of 2-3 distinct tau conformational strains with CBS samples showing increased protease resistance at specific cleavage sites and distinct cryo-EM fibril morphologies compared to PSP.
5. Spatial transcriptomics revealing CBS-associated microglial clustering within 50μm of tau-positive neurons with upregulated phagocytic gene expression (>3-fold vs controls).
6. Machine learning classifier achieving >90% accuracy in distinguishing CBS from PSP samples based on integrated multi-omics signatures from astrocytes and oligodendrocytes.
Success Criteria
• Identify minimum 3 cell-type-specific gene expression modules (>100 genes each) that significantly distinguish CBS from PSP with FDR < 0.05 and effect size > 1.5
• Detect statistically significant correlation (r > 0.6, p < 0.01) between tau structural conformations and cell-type-specific transcriptional signatures within the same brain regions
• Achieve successful single-nucleus library preparation from >80% of tissue samples with >5,000 high-quality nuclei recovered per sample and <15% mitochondrial gene contamination
• Demonstrate reproducible regional molecular signatures with >70%
...
• Identify minimum 3 cell-type-specific gene expression modules (>100 genes each) that significantly distinguish CBS from PSP with FDR < 0.05 and effect size > 1.5
• Detect statistically significant correlation (r > 0.6, p < 0.01) between tau structural conformations and cell-type-specific transcriptional signatures within the same brain regions
• Achieve successful single-nucleus library preparation from >80% of tissue samples with >5,000 high-quality nuclei recovered per sample and <15% mitochondrial gene contamination
• Demonstrate reproducible regional molecular signatures with >70% consistency across biological replicates within each disease group and brain region
• Validate spatial organization patterns showing significant association between tau pathology locations and glial activation signatures within 100μm proximity zones
• Generate integrated multi-omics classifier with cross-validated accuracy >85% for CBS vs PSP discrimination and identification of top 50 predictive molecular features