Making the $10 Genome a Reality

Despite the growing demand for genomic data and the falling price of genome sequencing, costs continue to restrain its use. Single Technologies, which is developing a three-dimensional approach to sequencing, believes it can cut the cost to just $10 per genome for consumables. That’s a drop from estimates of about $600 today. We spoke to Johan Strömqvist, CEO and founder of Single Technologies and Bob Kain, advisor to the company, about its 3-D Sequencing, how it works, and how this can open up expansive use of the technology.

Daniel Levine: Johan, Bob, thanks for joining us.

Bob Kain: Thanks, Danny.

Johan Stomqvist: Yes, thank you Danny.

Daniel Levine: We’re going to talk about genome sequencing, Single Technologies, and its efforts to bring about a $10 genome. I think most of us think of DNA in a linear fashion, a string of As, Cs, Gs, and T’s, but I thought it might be useful to start with the evolution of sequencing technology and how it’s moved from one dimension to two dimensions, and now with your technology, three dimensions. Can you start with the notion of dimensionality with regards to sequencing and how we’ve moved through that progress?

Bob Kain: Yes, I’m happy to talk about that. I’m going to go back to when sequencing technology was first invented. So the sequencing space really has evolved like many other technologies on exponential curves, we often call these Moore’s law technologies. The semiconductor technology is a great example and it moved from vacuum tubes to transistors to integrated circuits and eventually to today to three dimensional integrated circuits in order to basically improve performance and reduce the cost. And these technologies, the miniaturization really was critical in reducing the cost and improving the throughput of these systems. DNA sequencing is similar in that it started in the 1980s based on sort of macro two dimensional gels and the feature sizes were on the order of millimeters. The cost per base of a single DNA base back then was around a dollar. As you can imagine, to sequence a genome, which is 6 billion bases, it would’ve been pretty expensive, especially since you really can’t sequence a genome by just sequencing 6 billion bases. You need to sequence over a hundred billion to assemble a full genome. During the Human Genome Project, which started in early nineties, a new technology was invented called capillary sequencing. Capillary sequencing was still really a macro sequencing technology, but the feature sizes were reduced to sort of tens of millimeters and by going through capillaries, the volume of reagents and therefore the cost of reagents, was greatly reduced also. And it could be scaled linearly, not exponentially, but linearly from like tens of lanes to hundreds of lanes of sequencing at the same time. In this case, sequencing costs were brought down to around about $5 million, and the first genome was sequenced using this technology and completed in 2001 for many millions of dollars. During the nineties, another technology came around, which was really miniaturizing the analysis of RNA and proteins that was called microarray technology. Microrray technology really came around through the understanding that the ability to see a feature—a DNA feature or a protein feature—is limited, not so much by the strength of the signal, but by the background noise. Think of stars in the sky. When you’re in the city trying to look at stars at night, it’s very hard to see them, but if you go out to the desert where there’s no light pollution, all of a sudden the stars come out. So this realization helped drive the miniaturization of DNA detection, and that led to what we often think of as next generation sequencing. What’s interesting is that these DNA features were a million times smaller, or excuse me, a thousand times smaller than the features in the two dimensional gel arrays. And because you’re sequencing on an area that led to the square improvement of a thousand, which is a million times more features in the same area, this is important because now you’ve packed features into a much smaller area, you can image them more quickly and you use much, much less of the consumable or reagents in order to basically call those DNA bases. So, if you think about it, it’s like moving from the transistors of the 1960s to integrated circuits in the 1970s.

Daniel Levine: And how much of the cost of sequencing is in the reagents?

Bob Kain: Today, almost all the cost of sequencing is in the consumables, which are the reagents and the flow cell.

Daniel Levine: Single Technologies has developed Theta, which it describes as a 3D sequencer. What does it mean to sequence in three dimensions?

Bob Kain: So, let me just add that the microarray sequencing followed an exponential curve going from half a million dollars down to under a thousand dollars in 10 years by reducing the reagent costs, increasing the sequencing speed, and making sequencing in generally much more efficient, improving packing densities. But that curve really is now sort of falling off and Singles, what we often think of as 3D sequencing, takes that curve to a new level. And 3D sequencing is going from sequencing clusters on a single surface to sequencing clusters in the volume. That’s critical because if you think of reagent usage, when you’re sequencing on a surface, most of those reagents are not used as you flow by the surface. By basically assembling clusters in a three dimensional volume, you have a much more efficient usage of the same reagents, basically allowing you to sequence more DNA bases for the same reagent costs.

Daniel Levine: Does that have implications on speed as well?

Bob Kain: It does. It does have implications on speed with the innovations that have come up in Single’s optical system design. So it’s those innovations that allowed speed improvements as well as in a sense, three dimensional density improvements. And I’ll let Johan talk about that.

Johan Stomqvist: I think I would add that as we saw the side of this in NGS also as you park those clusters close and closer, you can at some states, you can’t really resolve them optically because there’s an optical resolution limit. And by doing in 3D, you can kind of overcome that limitation that you have. So that’s also one of the reasons why we can use more clusters per volume, so to speak. However, you need to have a really fast scanner, a three dimensional scanner to be able to separate those clusters in three dimensions. And there isn’t, or until now, there hasn’t really been any. I mean the speed of that 3D system exists. Yes, too slow. They have to be improved like thousand times if your conception are going to able to do it in 3D. That’s exactly what we’ve solved at Single. We have improved 3D with three zoom, four orders of magnitude.

Daniel Levine: And in terms of the problems this is solving with sequencing, is it just a matter of cost?

Bob Kain: Cost is important because getting the studies accomplished within a reasonable budget is necessary for enabling those studies, as well as a reasonable period of time. Those studies provide information that allows us to understand how our genomic information influences our health and understanding that basically leads to new and innovative applications such as the NIPT or newborn infant paternal testing and cell-free cancer testing. So as discoveries come about, companies then will use those discoveries to create new products and diagnostics, increasing the market for sequencing.

Daniel Levine: The vision here is to get sequencing of a genome down to $10. I take it, this is the lab cost, but even so, what would the implications of this be? How might it improve access to sequencing and broaden its use?

Bob Kain: So, one thing, there’s going to be a point where every baby born is sequenced, and as you can imagine, if sequencing costs $10, that point wouldn’t be far into the future and that information is critical. Babies born now are genetically tested for a handful of impactful genetic diseases. That testing would go away and sequencing would replace it. Additionally, a lot of the sequencing applications today are limited to the developed world because of costs. So reducing the cost of those applications would allow the applications to be adopted worldwide.

Johan Stomqvist: Maybe I could add on, I think also back in the days, I think it was perhaps hard to foresee the topology genome, what kind of applications showed up, for example, like NIPT, and Bob can elaborate on that, but I think it’s going to be exactly the same thing. Now when we drop the cost significantly, there’s some applications we just can’t foresee as I see it. But I would also add to cell-free DNA and so on, that there are new applications for using DNAs, more sort of an information carrier, a barcode for example, towards proteins to detect thousands of different proteins from 15 microliters of blood. And these type of measurements you perhaps want to do them regularly on a daily basis compared to human genome, which you might want to do once in your lifetime. So, we see these new types of applications you have in single cell, people want to do large populations of those. So there are probably a lot of applications that we can’t even foresee at this stage. I think that’s an important aspect as well. And also the fact that AI is coming and it’s a lot of companies building up databases and training algorithms, you need to have a huge set of data, high quality data to be able to support those algorithms.

Daniel Levine: This is technology that actually started as part of an effort to view protein-protein interactions. How did you come to see the potential to apply the technology to DNA and what led to the creation of Single?

Johan Stomqvist: Yeah, so protein-protein interaction was something I was working on during my PhD. Formed a company where me and my co-founders were studying how different compounds modulated protein-protein interactions actually in live cells and confocus setup was really an efficient way of platform to use for those applications. Turned out that each measurement took hours per cell, actually per pig cell, per cell. So it was very hard and time consuming to work on that type of imaging setup. So in 2014, I teamed up with two guys from the fiber optic industry and we started to rethink how to do three dimensional imaging. We came up with basically a new idea of doing imaging quite soon. So, we formed Single at that time. Quite soon afterwards we met a professor, Mads Nielsen. He’s a pioneer in sequencing DNA directly in cells, which is he’s now an advisor at 10X Genomics and he’s made a lot of progress there. We met him quite early on and he convinced us to go towards sequencing rather than protein-protein interactions. So that’s the backstory.

Daniel Levine: Bob, you spent 15 years at Illumina, you were chief engineering officer. What was your reaction when you first heard what the company was seeking to do?

Bob Kain: Well, it was interesting. Since I left Illumina in 2014, I’ve been consulting with many other companies in the sequencing space and talking to even more. And as you can imagine, a lot of the companies I speak with have interesting and innovative technologies, but it’s often very early on and they haven’t done much yet to sort of prove out the concept. So a lot of these technologies, when they work to prove out the concept, they sort of go away. I was first introduced to Single in August of 2023, and I was really intrigued by their innovations in the 3D sequencing and in the confocal imaging approach, but I really didn’t at the time understand where they were and assumed that they were very early. Over the next few months through additional conversations, I really realized that the company had been working on this technology quietly for about seven years, and they have built prototype systems and really proved out all of the technology limitations that I could foresee. So, at that point, I decided to go visit Single. I went to Stockholm in December, which might not be the best time of year to visit Stockholm, but the city is still beautiful and I really had a good time. I enjoyed meeting their team there. I enjoyed speaking with the team. We had a lot of one-on-one interactions over the next few days where they answered all my questions, sort of showed me the prototype, and we discussed the issues they faced and the data they received. And I came away really excited. By the time I got home, I decided that this technology was probably the first truly disruptive technology I’ve seen in this space since the HighSeq architectural system that was proven out by the HighSeq roadmap that got us to the thousand dollar genome. Single’s technology allows us to follow a new roadmap for cost per base and performance. They could get us to genomes under $10, which is really revolutionary. And so that’s what got me excited.

Daniel Levine: Walk us through the process of going from a sample to sequence. How does the technology work?

Johan Stomqvist: Yeah, so you start like most sequences today with a typical, what we call, Illumina library. It’s a format that is designed for the market leader’s sequencer. You create what we call the matrix. It’s a three dimensional structure where you distribute all the DNA. We have this device for that. And finally, you sequence with what is called SPS chemistry, sequencing by synthesis. The challenges to sequence in volumes are that you need to immobilize the DNA in three dimension. You need to be able to exchange radiance, like modified nucleotides, polymerase within this volume. And then finally, you need to image that this three dimensional structure at least a thousand times faster than you can do today. So this is exactly what we’ll be building during these years. And in the end, you essentially generate a FASTQ file, a standardized file. So you go from a physical DNA and you end up with a digital version of it stored in our servers.

Daniel Levine: And how large are the reads?

Johan Stomqvist: I mean, you can pack within this roughly 250 microliters of volume, roughly 1000 billions of clusters, essentially. So it has a capability of going there. We don’t do that much right now, but that’s what it allows you to do.

Daniel Levine: Well, what’s known about the accuracy of the technology and how it might compare to alternatives?

Johan Stomqvist: Yeah, so essentially I would say the way you sequence the polymers is that essentially will govern your… and of course the process. There’s something called fazing. All of these aspects will affect your quality. We basically try to utilize existing ways of doing things when it comes to chemistry. So there’s not much of a difference when it comes to quality in terms of let’s say the market leader. However, we’re still working on making the results more reproducible and stable.

Daniel Levine: And where are you in development? What will it take to get you to a commercial launch?

Johan Stomqvist: Perhaps you can say something about it since you see the system, Bob.

Bob Kain: Yeah, I can certainly give my opinion on this. So Single has what I would think of as a robust prototype. It looks like a production system, but it really needs to go through another round of development to improve serviceability, ship ability, reliability, robustness, all those key factors that are necessary for placing products externally. But it is perfect in its current design for running proof of principle experiments and working with collaborators on applications. And so I would say that they’re probably, once they push the button on the final production system, they’re a year to 18 months away to having a system that they can place externally and deal with all of those factors that I just mentioned.

Johan Stomqvist: Assuming we get the funding, of course that’s important, Danny.

Daniel Levine: How is the company funded and what’s the plans for raising additional capital?

Johan Stomqvist: Yeah, I mean, we raised roughly $20 million and we actually, we tried to raise capital at this moment and we have some potential leads and great conversation. We hope we can close it by the second half of this year.

Daniel Levine: Johan Stomqvist, CEO and founder of Single Technologies and Bob Kain, advisor to Single Technologies. Johan, Bob, thanks so much for your time today.

Bob Kain: Thank you, and thanks for having us.

This transcript has been edited for clarity and readability.

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