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Quantum-Si Lining Up Early-Access Users for Proteomic Platform in Advance of 2022 Launch

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NEW YORK – Proteomics startup Quantum-Si has lined up a number of early-access users for it semiconductor-based protein sensing system in anticipation of a 2022 launch.

As of the middle of November, the Guilford, Connecticut-based company had placed instruments at 10 outside institutions, with researchers exploring its utility for purposes ranging from cancer early detection to identifying protein signatures linked to long COVID.

Founded by Jonathan Rothberg, who previously started next-generation sequencing company Ion Torrent, Quantum-Si aims to similarly apply semiconductor chip technology to protein sequencing, allowing for single-molecule analysis of proteins, including post-translational modifications.

The platform consists of two modules, a sample preparation component called Carbon and a semiconductor chip-based analyzer called Platinum. The Carbon module is a microfluidic system that uses a variety of existing sample prep techniques including target enrichment and sample depletion to reduce the dynamic range of proteomic samples prior to analysis by the Platinum system. In September, the company announced it was collaborating with Protein Evolution, another firm founded by Rothberg, to develop new classes of affinity reagents to use as part of the sample prep process.

The Platinum system runs in two modes — a protein sequencing mode, in which the instrument analyzes individual proteins by sequencing their individual amino acids, and what the company has called a "digital analyte" mode, an affinity agent-based approach in which the wells of the assay chip are functionalized with different affinity agents like antibodies, and analytes are identified by observing the kinetics of their binding to those agents.

Quantum-Si tackles protein sequencing by using probes to different amino acids, including modified amino acids, and observing the binding of those probes across proteins of interest. Key to the technology is that the semiconductor-based sensing device does not rely on observing the color of the probes for detection, but rather measures the timing of light emissions following excitation of the target molecules with a laser, which, according to the company, makes it feasible to distinguish between the large number of amino acids and modified amino acids required for protein sequencing.

The device similarly relies on the timing of light emissions when operating in the digital analyte mode, measuring them to collect data on target binding to enable identification of the targets.

Quantum-Si has not disclosed all of its early-access users, but it cited several during its Q3 2021 earnings call, with CEO John Stark highlighting work at the Ecole Superieure de Physique et de Chimie Industrielles (ESPCI) in Paris to validate protein sequencing data first generated internally by Quantum-Si.

In a statement, Andrew Griffiths, a professor at ESPCI, said that his lab had successfully replicated and validated this sequencing data and is now using the system to support efforts in "single-molecule screening for directed evolution, parallelized single-molecule counting applications, and single-cell multiomics." Griffiths did not respond to requests for further comment on his work with the system.

Andrew Adey, an associate professor at Oregon Health & Science University and another of Quantum-Si's early-access users, told GenomeWeb he is interested in the possibility of collecting proteomic data without having to get involved with mass spectrometry.

"I've always been interested in getting some proteomics work going in my group," he said. "There's always been a barrier because it has been mass spec or nothing. And things never really materialized [for us] with mass spec. It was challenging, and it just didn't work out with a lot of projects."

For instance, Adey said his lab had tried, through a core facility, to use mass spec to look at protein aggregates to see what proteins were present and how they were associated in certain regulatory complexes but came away from the experience unsatisfied.

"It would have taken having someone in the lab who was really interested in wanting to push that to make it happen," he said. "It was just one step too inaccessible for us."

Adey added that not having mass spec expertise in his lab limited his team's ability to experiment and improvise.

"Core facilities are great, but sometimes we want to do weird different things and tinker, and that is not something that I would ever feel comfortable doing on a mass spec instrument," he said.

Quantum-Si's technology interested Adey in that it applies a sequencing approach to protein analysis, which he said fit in his "comfort zone" given his extensive experience with DNA sequencing.

In a July interview, Stark told GenomeWeb that while the company ultimately aims to be able to detect and distinguish between all 20 amino acids including modified forms, it is not at that level yet. He said that it had calculated that if it were able to detect between 10 and 12 different amino acids, it would be able to measure roughly 80 percent of the proteome.

Adey said his lab has only recently begun running experiments on the platform and so he couldn't speak extensively about its data quality but said he found the simplicity, low cost, and potential scalability of the system appealing. Quantum-Si plans to sell the platform for around $50,000, and during the company's Q3 call CFO Claudia Drayton said it projected annual consumables spending of around $45,000 per instrument.

Adey said that his lab's initial work with the platform will focus not on de novo protein sequencing but rather on more targeted protein assays looking at, for instance, perturbations in different signaling pathways.

"I think another thing we want to do is just kick the tires on the instrument and see what it can do," he said. "We might just throw some random things at it and see what we get. There are a lot of things we just want to try with it just to explore its capabilities."

Another early-access user, San Carlos, California-based IncellDx, is using Quantum-Si's platform for work on individuals with long COVID, exploring the notion that long-term COVID-19 symptoms could be caused by spike proteins from the virus lingering in monocytes post-infection.

In June, IncellDx and collaborators published a bioRxiv preprint in which they said they found spike protein in monocytes in patients with severe and long COVID-19 up to 15 months after infection. Bruce Patterson, CEO of IncellDx, said the company is using the Quantum-Si platform to look at the spike proteins in the cells of patients with long COVID.

He said the company plans to use the platform to look at spike protein variants "because we actually found some mutations in the S1 protein that may contribute to the pathogenesis of S1 in acute COVID and long COVID."

IncellDx is using flow cytometry to sort cells of interest and then using the Quantum-Si platform to analyze the spike protein and potentially other proteins in populations down to 5,000 to 10,000 of these sorted cells, Patterson said.

He said that he and his colleagues have done the initial work confirming the presence of the spike protein in these cells using mass spectrometry but noted that he found that the Quantum-Si platform's simplicity and small footprint made it an appealing alternative.

"I think this instrument is going to be critical because of its ability to [look] amino acid by amino acid, especially since we have seen some amino acid substitutions," Patterson said, though he was not able to provide information yet around the quality of the platform's protein sequence data.

He said the company is in the process of preparing a manuscript on its COVID-19 work and that it plans to add data from the Quantum-Si platform to that paper before submitting it.

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