Next-Generation Biological Measurement
Combining label-free Raman spectroscopy with machine learning to solve biomedicine's fundamental data bottleneck, building the measurement infrastructure for AI-driven drug discovery and disease intervention.
The Challenge
Biomedicine is fundamentally a control problem: moving biological systems from diseased to healthy states. When control problems involve hundreds of interacting variables, artificial intelligence consistently outperforms rule-based systems that humans design. Biology is one of those highly dimensional problems.
Yet AI requires three pillars: clear reward signals (knowing what "healthy" looks like), exploration environments (testing interventions), and high-quality data (understanding system state). We are missing all three in biomedicine: we don't know which biomarkers truly matter, in vitro systems fail to recapitulate disease, and we only measure what we already suspect is important.
The key insight: these three pillars are fundamentally interconnected through a single bottleneck—how we acquire biological data. Our measurement methods define which phenotypes we can distinguish, which experimental models we can employ, and the data we can extract per experiment.
Our Technology
Label-Free Raman Spectroscopy
Synelligence's platform is built on Raman spectroscopy, a physics-based measurement technique that captures the intrinsic vibrational fingerprint of all cellular molecules without genetic modification, fluorescent markers, or prior assumptions about what's important.
Raman spectroscopy uses laser light to probe molecular vibrations. Every molecule has a unique vibrational signature based on its chemical bonds. When we shine laser light on a cell, the scattered light contains information about every molecule present—proteins, lipids, nucleic acids, metabolites—simultaneously.
Proven Technology
Raman spectroscopy's ability to capture comprehensive cellular states has been validated across multiple independent studies, demonstrating high correlation with gold-standard measurement techniques.
Raman Predicts RNA Expression Profiles with High Accuracy
From the study "Raman2RNA: Live-cell label-free prediction of single-cell RNA expression profiles by Raman microscopy"
Raman-predicted and scRNA-seq measured pseudo-bulk profiles show strong correlation (r = 0.969-0.988) across different cell types including iPSCs, epithelial, stromal, and MET cells. Each dot represents a gene, demonstrating that Raman spectroscopy can accurately predict transcriptomic profiles without genetic modification.
Monitoring Cell State Transitions Without Labels
From the study "Non-invasive monitoring of T cell differentiation through Raman spectroscopy"
Raman spectroscopy tracks T cell activation and differentiation over 5 days with high correlation to surface markers (CD25/CD69 for activation, CD44/CD62L for differentiation). UMAP visualizations show clear separation of cellular states, demonstrating Raman's ability to monitor dynamic cellular processes in real-time.
Tracking Drug-Induced Molecular Kinetics
From the study "Revealing chemical processes and kinetics of drug action within single living cells via plasmonic Raman probes"
Raman spectroscopy monitors drug-induced cellular changes over time by tracking eight characteristic molecular events including protein denaturation (502cm⁻¹), modification (1308cm⁻¹), degradation (1129cm⁻¹), and DNA fragmentation (836cm⁻¹). This demonstrates the potential to study dynamic chemical reactions within cells under treatment with any drug, providing unprecedented insight into drug mechanisms of action.
Our Approach
Synelligence's insight: Raman spectra essentially compress the full cellular state into a high-dimensional spectral space that can be decoded into many functional readouts. Rather than choosing which biomarkers to measure upfront, we capture everything and let machine learning determine what matters for predicting cellular state transitions.
Comprehensive Capture
Measure all cellular molecules simultaneously without modification or assumptions
Machine Learning
Let AI determine which molecular signatures predict cellular state transitions
Predictive Validation
Validate against preclinical datasets and biobank samples for drug efficacy and toxicity