{"id":6245,"date":"2021-08-01T03:47:00","date_gmt":"2021-08-01T03:47:00","guid":{"rendered":"https:\/\/41j.com\/blog\/?p=6245"},"modified":"2021-07-11T03:47:36","modified_gmt":"2021-07-11T03:47:36","slug":"quantumsi-prospectus-review","status":"publish","type":"post","link":"https:\/\/41j.com\/blog\/2021\/08\/quantumsi-prospectus-review\/","title":{"rendered":"QuantumSi Prospectus Review"},"content":{"rendered":"\n

This post originally appeared on the substack<\/a>. <\/p>\n\n\n\n

I previously looked at QuantumSi\u2019s protein sequencing approach<\/a> back in September. But recently someone forwarded me their prospectus<\/a>. Having recently reviewed Nautilus<\/a> it seems like a good idea of revisit QuantumSi. In this post I provide an update based on my previous thoughts but you may want to refer to that post<\/a> for details from their patents.<\/p>\n\n\n\n

Technology<\/h2>\n\n\n\n

The basic process QuantumSi use to sequence proteins can be briefly described as follows:<\/p>\n\n\n\n

  1. Fragment proteins into short peptides, and isolate in wells.<\/li>
  2. Attach a label to the terminal amino acid, and detect the label.<\/li>
  3. Remove a single terminal amino acid.<\/li>
  4. Go to step 2 to identify the next amino acid.<\/li><\/ol>\n\n\n\n

    At a high level is not unlike single molecule sequencing-by-synthesis, in that monomers are detected sequentially. The difference here being that rather than incorporating monomers, in this approach they are cleaved. QuantumSi appear to fragment the proteins prior to sequencing. I assume this is to avoid secondary structure issues. But it does mean they are getting fragmented sequences rather than an end-to-end sequence for the entire protein.<\/p>\n\n\n\n

    \"\"<\/a><\/figure><\/div>\n\n\n\n

    When I reviewed their patents, it was reasonably clear that you\u2019d be unlikely to get an accurate protein sequence. It\u2019s more likely to be a fingerprint. This means that rather than being able to call a \u201cY\u201d, you\u2019d likely be able to say this amino acid is one of \u201cY,W or F\u201d.<\/p>\n\n\n\n

    The Prospectus suggests that can resolve these ambiguities by looking at transient binding characteristics. The \u201caffinity reagents\u201d they use don\u2019t bind and stay attached. Rather they have on\/off binding. So you\u2019ll see them attach, generate a signal, then detach, then another one bind etc. Ideally a reagent that binds to \u201cY,W or F\u201d might bind more strongly to one (e.g. Y) than another (e.g. W) and you can use that information to infer the amino acid type.<\/p>\n\n\n\n

    As mentioned in my previous post, they use fluorescent lifetime determine which affinity reagent is bound. So for every detection event they have two pieces of information, the affinity reagent type (from fluorescence lifetime, and intensity) and the binding kinetics (from the on\/off rate). They call this 3 dimensional data (fluorescence life time, intensity, and kinetics).<\/p>\n\n\n\n

    \"\"<\/figure><\/div>\n\n\n\n

    The nice thing about this is that while you will need various reagent types, you don\u2019t need a complex fluidic system and you are observing, and classifying them in real time. <\/p>\n\n\n\n

    However, I\u2019ve not seen anything that suggests the classification works well enough to give the full sequence. And they state that this \u201cwill ultimately enable us to cover all 20 amino acids\u201d. Suggesting that they currently can\u2019t.<\/p>\n\n\n\n

    Overall, the above approach is in line with my previous speculation based on their patents.<\/p>\n\n\n\n

    Chips<\/h3>\n\n\n\n

    Like Ion Torrent, they make a big deal out of using semiconductor fabrication for their sensor: \u201csimilar to the camera in a mobile phone, our chip is produced in standard semiconductor foundries\u201d. I generally take issue with this argument. Semiconductor fabrication is great. But if you can\u2019t reuse the sensors it\u2019s more like buying an expensive camera, taking one picture, then throwing the camera in the trash.<\/p>\n\n\n\n

    This isn\u2019t to say that semiconductor sensing isn\u2019t interesting\u2026 but there are other issues that need to be considered. They also talk about Moore\u2019s law, suggesting that if \u201cMoore\u2019s Law remains accurate, we believe that single molecule proteomics\u2026will allow our technology to run massively parallel measurements\u201d. Aside from Moore\u2019s law clearly being in trouble, this doesn\u2019t make much sense, as there are other physical limits involved here.<\/p>\n\n\n\n

    From various public images, I\u2019d guess the chip is ~15 to 20mm\u00b2. PacBio chips (which use a very similar approach) have ~8M wells on a chip that appears to be roughly the same size. I didn\u2019t see an explicit statement on read count, other than \u201cparallel sequencing across millions of independent chambers\u201d. But my best guess would be in the 10M range.<\/p>\n\n\n\n

    This puts them at the low end of throughput as compared with Nautilus and other next-gen proteomics approaches.<\/p>\n\n\n\n

    \"\"<\/a><\/figure><\/div>\n\n\n\n

    Product<\/h2>\n\n\n\n
    \"\"<\/a><\/figure><\/div>\n\n\n\n

    The product has 3 components. A sample prep box (Carbon) the sequencing instrument (Platinum) and a Cloud based analysis service. Unlike Nautilus they suggest that primary data analysis happens on instrument. The instruments combined pricing is supposed to be in the $50,000 range, which is relatively cheap.<\/p>\n\n\n\n

    Commercial Stuff<\/h2>\n\n\n\n

    QuantumSi say they have already initiated their early access program, but I\u2019ve not heard of anyone else talking about this publicly. They are aiming for a commercial launch in 2022. And say that their addressable market is $21 billion. This breaks down as follows:<\/p>\n\n\n\n

    \"\"<\/a><\/figure><\/div>\n\n\n\n

    Of this, I think the true addressable market is closer to the $5B legacy proteomics segment. It doesn\u2019t seem realistic to use the proposed approach for health care\/diagnostics in its current form. Partly because the per-sample COGS is likely pretty high, and partly because for these applications you may want a higher throughput instrument.<\/p>\n\n\n\n

    They also suggest that in the future they will release lower cost instrument for at home testing:<\/p>\n\n\n\n

    \"\"<\/a><\/figure><\/div>\n\n\n\n

    Conclusion<\/h2>\n\n\n\n

    QuantumSi\u2019s approach is closer to sequencing than Nautilus, but I suspect the platform will still not give a true amino acid sequence when initially released. For the reasons highlighted in my previous post<\/a> protein sequencing is just much much harder than DNA sequencing. So, like Nautilus what they\u2019re developing may be more of a protein fingerprinting device, where traces are compared against a database of known proteins.<\/p>\n\n\n\n

    This begs the question: what\u2019s the value in a relatively low throughput protein fingerprinting instrument? Where exactly the throughput spec needs to be set to be useful, particularly for diagnostic applications isn\u2019t clear to me. But 10 million reads would certainly seem to be on the low end. I\u2019ll try and address this in a future post.<\/p>\n","protected":false},"excerpt":{"rendered":"

    This post originally appeared on the substack. I previously looked at QuantumSi\u2019s protein sequencing approach back in September. But recently someone forwarded me their prospectus. Having recently reviewed Nautilus it seems like a good idea of revisit QuantumSi. In this post I provide an update based on my previous thoughts but you may want to refer to that post for details from […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[1],"tags":[],"class_list":["post-6245","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/p1RRoU-1CJ","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/41j.com\/blog\/wp-json\/wp\/v2\/posts\/6245","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/41j.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/41j.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/41j.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/41j.com\/blog\/wp-json\/wp\/v2\/comments?post=6245"}],"version-history":[{"count":1,"href":"https:\/\/41j.com\/blog\/wp-json\/wp\/v2\/posts\/6245\/revisions"}],"predecessor-version":[{"id":6246,"href":"https:\/\/41j.com\/blog\/wp-json\/wp\/v2\/posts\/6245\/revisions\/6246"}],"wp:attachment":[{"href":"https:\/\/41j.com\/blog\/wp-json\/wp\/v2\/media?parent=6245"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/41j.com\/blog\/wp-json\/wp\/v2\/categories?post=6245"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/41j.com\/blog\/wp-json\/wp\/v2\/tags?post=6245"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}