Technology
To unlock the untapped potential of proteomics, making incremental improvements to existing technologies wasn’t enough. So, we rethought everything. Nothing was sacred and no step of the process was too small to be reimagined.
The result is a large-scale, single-molecule platform with the potential to achieve quantification of >95% of the proteome.
Our platform aims to:
- Deliver unprecedented sensitivity
- Produce robust, reproducible, and complete data
- Have a run time of days, not weeks
- Be fully integrated with an end-to-end workflow
- Be easy to use
Integrated, Single-Molecule Proteome Analysis
The Nautilus proteome analysis platform leverages a nanofabricated, large-scale, single-molecule protein array, multi-cycle imaging, and machine learning analysis to potentially identify and quantify the proteome with extreme sensitivity and scale. First, individual, intact protein molecules are immobilized via conjugation to proprietary scaffolds for deposition onto an array with billions of landing pads. Next, multi-cycle imaging allows repetitive, non-destructive probing of individual proteins with unique binding reagents. The results are digitized and analyzed to decode the proteome, potentially enabling quantitative analysis at unprecedented scale over an exceptionally large dynamic range.
Protein Array Preparation
Isolated protein from blood, tissue, or cells is bound to a proprietary scaffold for single-molecule, single-protein deposition onto one of the 10 billion landing pads found on the Nautilus array. The array’s hyper-dense surface is designed with the goal of allowing identification and quantification of millions of individual protein molecules simultaneously.
On Instrument Workflow
The protein array is loaded onto the platform where fluorescent affinity-probes flow over the array to bind short, defined protein motifs and binding events are measured. Since samples are not degraded, they can be washed and probed repeatedly with a variety of unique affinity-probes, each designed to target specific motifs on multiple proteins. Every new cycle provides unique information and increases the resolution of individual protein identity. Using this approach, the platform aims to sensitively measure tens of billions of molecules across hundreds of cycles in just one day.
Machine Learning Analysis
Our machine-learning analysis software is designed with the goal of converting binding information to protein identities and quantities. The binding results from each cycle are digitized and our software is used to decode the binding sequence into broadscale proteomics information. Our ability to decode the proteome will continually improve as the database grows.
