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Many people are familiar with cell and gene therapy and genomics, but less so with proteomics. How will what you are doing with proteomics help in the fight against disease?
The key to proteomics is understanding the massive variation in proteins. There are about 20,000 genes that encode approximately 200,000 transcripts, which in turn encode millions of proteins. Estimates suggest anywhere from one to five million proteins.
Proteins play a crucial role in disease. Millions of years of evolution have given function to these proteins, and we don't fully understand them yet. Genomics is your risk—your blueprint—while proteins are your status—what's happening with you right now. They're changing all the time.
Understanding that complexity is really hard, especially compared to the genome, which has four chemical bases—A, T, C, and G—that you can amplify and sequence cheaply. Proteins are made of 21 amino acids, the average protein is approximately 470 amino acids long, they fold in different ways, and you can't amplify them like DNA.
So it's a lot harder to study, but it's a super important piece of the puzzle. We've seen the most interest and effectiveness in complex diseases where genomics alone hasn't penetrated deeply. Neurodegenerative diseases like Alzheimer's and Parkinson's, where there aren't clear genomic markers, are areas of interest. Oncology is another area with a lot of interest, as well as aging.
What is your Proteograph Product Suite and Analysis Suite?
It consists of our assay kits based on our proprietary nanoparticle technology. Our CEO, Dr. Omid Farokhzad, spent 20 years at Harvard studying nanoparticles. The big discovery was how these proprietary nanoparticles enrich plasma samples so you can analyze proteins. Then we have an automation instrument, manufactured by a third party but designed by us. It takes the assay kit, nanoparticles, and associated reagents, mixes them with the samples, and runs through a defined, validated and verified protocol. Depth, speed, scale and reproducibility in an unbiased way - that's our secret sauce.
You put a sample in, get peptides out, and then those peptides are run on a mass spectrometer. We're detector-agnostic; our detector today is a mass spec, and we use all the major mass spec providers - Thermo Fisher, Bruker, and SCIEX (part of Danaher).
The data coming off the mass spec is fed into our Proteograph Analysis Suite, a cloud-based data analysis tool that allows you to take the raw MS data files and gain biological insight - identify which proteins are upregulated or downregulated at scale.
Before the Proteograph, the largest deep, unbiased proteomic study we could find in the literature was 48 samples, which took months because of all the depletion and fractionation steps.
Now, we have customers doing studies with thousands of samples. Our spin-out, PrognomIQ, is working on a 15,000-sample prospective study using deep, unbiased proteomics as well as other omics. It's a scale that wasn't imaginable a few years ago.
You’re a fairly young company, but already making an impact?
Proteomics has garnered a ton of interest lately. With targeted technology, you have to know what you're looking for, and they're mostly great if you know what you are looking for. With our technology, you can broaden the aperture to go beyond pre-defined sets of target proteins, naturally capturing unknown proteoforms.
The Proteograph Product Suite has been broadly commercially available for about two and a half years, and we have 19 publications and 10 bioRxiv manuscripts out there. These are high-impact papers in high-impact journals. As more evidence grows of what you're able to do with the Proteograph, more people will want it.
How have your customers been using Seer's products?
Our technology was used for a study involving SpaceX astronauts. They took blood samples right before takeoff, upon landing, and at regular intervals post-flight. They found that there were a number of dysregulated proteins—either upregulated or downregulated—and even six months later, many of those proteins were still dysregulated. It suggests that even short exposure to space travel may create lasting changes in protein function.
There was a fascinating Mass General study, looking at an Alzheimer's cohort of approximately 1,600 patients. We found 138 biomarkers associated with Alzheimer's compared to healthy patients. Of those, 44 were already known markers, but 94 were new putative biomarkers, providing a rich potential pathway for discovery. Interestingly, 55% of those 138 markers weren't on the leading targeted panel - you couldn't see that content without an unbiased approach. They also identified eight biomarkers that could determine whether a patient would be a rapid or slow decliner in Alzheimer's progression.
Another exciting area is xenotransplantation research conducted by Dr. Brendan Keating at NYU. He is studying the transplantation of pig organs into humans. Our technology is species-agnostic, so we can distinguish between species in the same blood sample. He implanted pig hearts into two brain-dead human patients and monitored protein expression over time. He could see the degradation of pig proteins alongside changes in human proteins, offering invaluable insights into the transplantation process.
PrognomIQ is our spin-out. They set out to do a multi-omics study for early cancer detection, specifically early detection of lung cancer in high-risk populations—people who are current or former smokers over 40. The standard protocol is to get a CT scan every year, but compliance is low. Their test aims to use a blood draw to determine if you should be referred for a CT scan.
They took a multi-omics approach—genomics, transcriptomics, proteomics, lipidomics, metabolomics—on an approximately 2,500-sample study using Seer's technology for proteomics. They came up with a set of markers with very high sensitivity and good specificity for lung cancer—best-in-class data. The most prominent markers driving their classifier were the unbiased proteomics markers they discovered using our technology. They're now working on turning that into a diagnostic test. It's pretty amazing; they got spun out in 2020, and four years later, they're already developing a test.
How does AI and machine learning fit into proteomics?
These are extraordinarily complex datasets, especially with large-scale studies involving thousands of samples. We see things at the peptide level; when our particles capture the whole protein, we digest it into peptides—that's what we analyze. We get a ton of information, and teasing out the signal from the noise is complex.
AI can play a significant role in processing these datasets. We already use machine learning algorithms, but we're not yet at the point where you can just type into a database and get all the answers. It's something we're working on and have made great progress, but there's still a ways to go.
For 2025, what are the main goals for Seer?
Continuing to demonstrate the power of the Proteograph Product Suite through peer-reviewed publications is important and a focus. We'll drive interesting studies to differentiate our technology. We've got some exciting stuff in the pipeline that I can't share right now, but as more people get access to the technology, they come up with creative applications we hadn't even thought of.
We'll also continue to drive product innovation, getting feedback from our customers and staying ahead in a competitive market. Another goal is reducing barriers to access—allowing people to get this data into their hands. We have programs like the Seer Technology Access Center (STAC), where you can send us a sample, and we'll run it through our lab and provide the data. The goal isn't to become a service business but to drive interest in bringing the Proteograph technology in-house.
We also have our Strategic Instrument Placement (SIP) program, where we loan an instrument if you purchase kits upfront. It's a way for customers to access the technology without the initial capital expenditure, run the data, and eventually purchase the instrument if they like it. We've already had several customers do that.
You made the switch from banking to health sciences, how have you found that culture change?
I met Omid, our CEO, in early 2019 through mutual acquaintances. As a banker, I was always looking for the next interesting company, and I was intrigued by what Seer was doing.
As I learned more about the technology, I got super excited about Seer and the macro trend of analyzing proteins in depth and at scale. I started thinking about what I wanted to do for the next 20 years. I could have stayed at Morgan Stanley, but I thought it would be really cool to jump into a company, exercise a new set of muscles, and build something in a fascinating area.
I joined Seer in May of 2020. What's really exciting for me is the discussions around population-scale studies. We're talking to groups who want to do studies with 10,000, 20,000 and even 100,000 samples. If you combine genomics and proteomics on that scale, the biological insights will be incredibly revealing and impactful.
