NEW YORK – Startup Quantum-Si last week introduced what it has termed its next-generation protein sequencing platform at the Cowen Healthcare 2020 meeting.
Founded by Jonathan Rothberg, one of the pioneers of next-generation nucleic acid sequencing, and backed by more than $150 million in funding, the Guilford, Connecticut-based company is the latest of several firms to announce plans to bring a sequencing-based approach to proteomics.
According to President and Chief Operating Officer Michael McKenna, the firm's technology uses a sequencing approach it calls Quantum Si Time Domain Sequencing combined with a semiconductor sensing device. McKenna said that the advantage of this approach is that it does not rely on observing color for detection but rather measures the timing of light emissions, which is something semiconductors are well suited to do. The approach allows for sequencing of proteins, including post-translational modifications, at the single-molecule level, he said.
"We think there is a whole new era of biomarker discovery and diagnostics to be derived from that technological capability," he said.
The company has not publicly described in detail the technology underlying the platform, but it has filed a number of patent applications describing aspects of the system. In an application filed in December (US Patent Application #20190383739), it described an assay chip composed of multiple sample wells, each corresponding to a sensor for detecting energy emitted from the sample wells following excitation from a light source.
The patent also describes "techniques for detecting single molecules using sets of luminescent tags to label different molecules," such as nucleotides or amino acids.
While biosensing assays using luminescent tags commonly rely on differences in the colors emitted by different tags to distinguish between molecules of interest, Quantum-Si's system, as McKenna notes, is not based on differences in color but rather temporal differences.
According to the patent, "measurements are based on exciting one or more markers (e.g., fluorescent molecules), and measuring the time variation in the emitted luminescence … Detecting the temporal characteristics of light emitted by markers may allow identifying markers and/or discriminating markers with respect to one another."
McKenna did not say when Quantum-Si hoped to launch the platform but said the company will provide more information on commercialization plans at a future date. In terms of the kinds of labs Quantum-Si expects will be early adopters, he said that the firm believes "it runs the gamut from people doing basic research to translational medicine."
McKenna said that the advantage of semiconductors is their ability to scale. While he said he could imagine doing a proteome-scale single-molecule analysis of a plasma sample, he didn't believe that this sort of unfocused approach would be where the instrument would prove most useful initially.
"I think the real utility of the technology is going to be on focused experiments with tissues that are relevant to the disease you are studying," he said.
Initially, he envisions the platform being used for targeted panels of proteins, especially in limited samples like exosomes or circulating tumor cells.
"I think our technical advantage is going to be the single-molecule sensitivity and the ability to work with extraordinarily small amounts of material, which I think is ultimately going to offer capabilities that no one has really seen in proteomics yet," he said.
In the company's presentation at the Cowen meeting, it showed an analysis of insulin using the platform, in which it detected and measured the abundance of wild-type insulin and a form of the protein containing a point mutation.
"The power of next generation [protein] sequencing is that you get a digital quantitative readout that allows you to see both the mutations that are present and their abundance, " said Matt Dyer, the company's chief product officer. Dyer also highlighted the platform's ability to identify post-translational modifications like phosphorylation, which is a major area of interest in proteomics and drug research but presents challenges to both mass spec and antibody-based approaches.
While proteins are the company's initial focus, Dyer said the core of the platform is based on the detection of light, which is central to modern diagnostics, thus paving the road for many other future applications that leverage the same sensor.
In addition to the analyzer, which the company has named Platinum, the platform includes an automated sample preparation module, named Carbon, which Dyer said will be able to process samples for both protein and nucleic acid analysis.
Quantum-Si is the most recent entrant to the still small but growing protein sequencing field. Several commercial companies are pursuing protein sequencing technologies, most of them, like Quantum-Si, with roots in the next-generation sequencing world.
One such firm is San Diego-based startup Encodia. Its cofounder Michael Weiner, like Rothberg, is an inventor of 454 sequencing. Also, the firm's CEO, Mark Chee, and its Vice President and Chief Technology Officer, Kevin Gunderson, are cofounders of Illumina and Gunderson is the former senior director of advanced research at Illumina.
Encodia is using a labeling and degradation-based approach to protein sequencing, developing DNA tags to label amino acids on peptides and then degrading these peptides one amino acid at a time. Upon removal, the DNA tags can be sequenced using conventional NGS, allowing for read-out of the tagged peptide sequences.
NGS firm Oxford Nanopore is also pursuing protein sequencing. The company has been active in this area for years but has disclosed little about its plans. However, at the Nanopore Community Meeting last December, Oxford Nanopore Chief Technology Officer Clive Brown provided a few details on the firm's work on protein sequencing.
The company's method uses the protein ClpXP to unfold a target protein and translocate it through a nanopore. The translocation of the protein through the pore generates a signal that might, in theory, be used to identify the protein and perhaps even determine its amino acid sequence, including the presence of post-translational modifications.
According to Brown, Oxford Nanopore is currently using a "slightly revised" version of this approach to analyze proteins on its droplet system and has generated now "significant numbers of fairly consistent protein" signals that it is using to train algorithms against purified protein libraries.