Key Features
Single-Cell Precision
Tumor Heterogeneity Mapping
Identify distinct cell populations to understand treatment resistance
Spatial Transcriptomics
Map gene expression with spatial context - 99% accuracy
Patient Avatars
Digital twins to predict treatment response before trials
Case Studies
Case Studies
Hidden Subtype Detection in Breast Cancer
Revealing Misdiagnosed Cancer Populations at Single-Cell Resolution
- Detected ER+ subclusters within TNBC-diagnosed tumors
- Identified TNBC populations in ER+-diagnosed patients
- Confirmed findings through biomarker validation
- Explained treatment resistance and recurrence patterns
Methodology:
- Single-cell RNA sequencing analysis
- AI-powered subtype classification
- Biomarker expression validation
- Comparison with bulk diagnostic results
Results:
- Revealed false negatives in traditional diagnostics
- Identified patients at risk of treatment failure
- Enabled more precise treatment selection
PROTAC E3 Ligase Optimization
Cell-Type Specific Ligase Selection
- Optimized E3 ligase selection at cellular resolution for PROTAC development.
ICI Response Biomarkers
T-cell Trajectory Analysis
- Mapped T-cell activation to exhaustion trajectory
- Identified novel biomarker candidates
Methodology:
- Pseudotime trajectory analysis
- TME profiling
Capabilities
Advanced Analysis Features
Multi-Omics Integration
Combine transcriptomics, proteomics, and metabolomics data for comprehensive cell profiling.
Cell Communication
Map cell-to-cell signaling networks and identify therapeutic intervention points.
AI-Powered Insights
Deep learning models automatically identify biomarkers and predict drug response.
Experience Single-Cell Analysis
See how our technology can transform your cancer research.