Jason Gammack on the Promise of Spatial Biology

Episode Summary 

Rapid and cheap DNA sequencing technology can tell us a lot about which genes a patient is carrying around, but it can’t tell us when and where the instructions in those genes get carried out inside cells. Resolve Biosciences—headed by this week’s guest, Jason Gammack—aims to solve that problem by scaling up a form of intracellular imaging it calls molecular cartography.

Gammack says the technology offers a high-resolution way to see the geography of gene transcription in single cells, that is, where specific messenger RNA molecules congregate once they’ve left the nucleus. The technology can trace up to 100 gene transcripts simultaneously. Right now it only works for mRNA, but the company says it plans to add the ability to track DNA, proteins, and “metabolic data layers.” The big idea is to make it easier to see how gene expression translates into normal tissue development and, by extension, the pathology of genetic or infectious diseases.

“We can go in and identify specific RNA molecules that code for a known protein,” Gammack tells Harry. “We can label those molecules and with high power microscopy and molecular biology and very importantly software, we can then identify and literally visualize individual RNA transcripts in the context of the cell and tissue.”

Resolve was in stealth mode from 2016 to December 2020, when it announced a Series A financing round of $25 million. Its technology is being tested by six teams of scientist-collaborators as part of an early access program launched in 2019. Resolve reportedly plans to launch its service commercially in the first half of 2021.

Gammack joined the company from Inscripta, where he was chief commercial officer helping to sell the CRISPR-based Onyx gene editing platform. Before that he was at Qiagen, a German provider of assays for molecular diagnostics such as a Covid-19 antigen test, where he was vice president of life sciences.

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TRANSCRIPT

Harry Glorikian: I’m Harry Glorikian, and this is MoneyBall Medicine, the interview podcast where we meet researchers, entrepreneurs, and physicians who are using the power of data to improve patient health and make healthcare delivery more efficient. You can think of each episode as a new chapter in the never-ending audio version of my 2017 book, “MoneyBall Medicine: Thriving in the New Data-Driven Healthcare Market.” If you like the show, please do us a favor and leave a rating and review at Apple Podcasts.

Harry Glorikian: We’ve come a long way in the last 25 years in our ability to sequence the DNA of individual patients. We can even see which genes are being expressed as RNA, the instructions for making proteins. But after that there’s a big blind spot in our understanding, because it’s still hard to see exactly which RNA molecules inside our cells actually get translated into proteins, and just as important, when and where they get translated. The problem is that almost everything that’s interesting about human biology and human disease happens inside that blind spot.

Resolve Biosciences in Germany is one of the new biopharmaceutical startups tackling that challenge. My guest this week is Jason Gammack, the CEO of Resolve, and he says the company has come up with a way to label multiple RNA molecules with probes that glow in different fluorescent colors.

Resolve built software that can decode the color patterns to see where RNA transcripts gather in the cell and how they’re involved in cell development. That kind of location information that could eventually produce a better picture of how normal cells grow, and also how that growth becomes cancerous and maybe even what kinds of drugs could stop tumors before they kill their hosts.

Gammack joined the company last year, around the same time the company announced a 25 million dollar funding round to help bring its so called “Molecular Cartography” technology to market.

Here’s our conversation.

Harry Glorikian: Jason, welcome to the show

Jason Gammack: Harry, it’s great to be here. Thank you.

Harry Glorikian: It’s been great talking to you and getting to know you. I feel like we should be doing this over a beer and we should be talking for hours. And my I’m sure, my 19 year old would be like, do you want to go to Germany? Let’s go to Germany.

Cause he loves coming there and having beers when, when when we’ve done it in the past Molecular cartography. I feel like, you know, Galileo is about to like, you know, step into this conversation with us, but for those people who don’t, who aren’t molecular biologists, it it’d be great. If you could sort of paint the bigger picture for us and, and help us understand what is, what is this concept of, I think spatial transcriptomics. I almost like stuttered on my words. And why is it important?

Jason Gammack: Yeah. And so it’s a great question Harry. And so again, thanks for the invite to join the the podcast. So context matters. Let’s start with that statement, reading a book without understanding the context makes it difficult book to read.

And if you think about our genome, the DNA that makes us similar and unique, it’s a book. And right now we don’t have full context of what that book is and Resolve Biosciences is a company, that’s focused on creating tools to help give context to the genome. And so let me explain that a bit. So the central dogma of biology is DNA.

Which is in your cells is made into RNA and that RNA is then translated into proteins and those proteins are in essence. What makes you, you, it’s your muscle? It’s your hair? It’s your skin. It’s your organ systems. It’s a lot. And we understand the book pretty well from the letters, a C, G and T. And we’ve been in an exponential phase of learning as it pertains to the genome and companies such as aluminium.

It’s a San Diego based biotech company has created a technology that allows us to sequence the entire human genome. So every letter in your genome, We can do that now in a couple of days and for a couple of hundred dollars and we need to keep that in context, you know, the first genome took…

Harry Glorikian: I remember yeah.

Jason Gammack: 15 years and $7 billion to do it. As a matter of fact, you know, this is the anniversary of that event happening, right?

Harry Glorikian: Yep.

Jason Gammack: So we’ve really learned a lot about the core code of the genome. But the disease, chronic disease still exists in our population. And so we have to ask the question, what else do we need to understand? And we at Resolve believe that the next question is really to understand where different genetic events are occurring within a cell.

The interesting thing. And the big question in biology is largely we all have the same DNA in our bodies. You know, humans are remarkably, remarkably homologous and the variation in humans is very, very low, but yet we have individuals who are six and a half feet tall. We have individuals that are four feet tall.

We have individuals that way, you know, 250 pounds and we have individuals that weigh 90 pounds. And so why. And even more perplexing is we have diseases such as cancer, where two women, can you present with a very similar breast tumor one, or they both can be treated with a very similar treatment, identical drops, and one can go into complete remission and eventually be here and the other cannot and potentially die.

Harry Glorikian: Right.

Jason Gammack: And so the question is why does that happen? And that has to come down to a number of different variables that we can’t yet measure. And so our belief at Resolve Biosciences is we are going to develop tools to help understand those differences. And that’s really urgent.

Harry Glorikian: So let’s, I mean, I’m trying to paint a picture for people that are listening to this. Right? So I think of this as, cause I feel like I’ve been to at least part of this movie before, when I started in immunohistochemistry, where we could actually visualize, you know, rather than grinding up a bunch of cells and looking at the moles and, you know, in breast cancer, we were able to actually stain the cells with antibodies that would specifically show us, you know, different parts of a cell that were lighting up. And that was, you know, sort of a flat file way to look at it with a certain level of resolution. And you’re, I think, zooming in to the molecular level now and taking it to a different resolution.

Jason Gammack: Absolutely. So that’s a, that’s a great point. And let me build on that one just a bit. So immune histochemistry opened the books to understand different types of disease status, where you can start profiling cell types and understand where they are in the cell cycle, which can be indicators for physicians or the biologists to prescribe a particular therapeutic. Right. We take that even to another degree.

I’ll use an analogy. It’s perhaps overused, but think about Google Maps. So Maps allows you to start at the continent or global level, and then focus in to this country. You focus it into a state, focus into a city, focus into a stream and even focusing. So our technology and the molecular cartography platform is similar in that we can take single cells or we can take tissues license and through our molecular biology approach, we can label individual RNA transference. So going back to that: DNA makes RNA makes protein. We can go in and identify specific RNA molecules, that code for a known protein. We can label those molecules and with high power microscopy and molecular biology and very importantly software, we can then identify and literally visualize individual RNA transcripts in the context of the cell and tissue.

So now going back to that Google Maps analogy, we now have that woman who has the unfortunate breast tumor. We can put sections of that breast tumor on the slide. We can use our molecular cartography technology to be able to look at the gene expression patterns within that tumor. And those patterns can give insights to researchers and eventually to clinicians in how to affect and treat that disease state very, very possible.

Harry Glorikian: So I, I, we’re talking about essentially creating a three-dimensional map of the cells and which ones are lighting up, which ones are not lighting up, how they affect each other, basically intercommunication between these cells,

Jason Gammack: And intra-communication inside the cell as well.

Harry Glorikian: So, so. Where, where are you? How far are you in this? I guess is the first question.

Jason Gammack: Yeah. Good question. So this journey didn’t just start. And so this journey started in 2016. When we at a previous company thought about this challenge of spatial loud. Again, you know, we have sequence genomes, but yet cancer persists in the population. And we were asking questions.

What’s the next answer that needs to be brought to science. And so in 2016, we brought together a truly gifted group of scientists to come up with solutions, to be able to look at the spatial relationship of gene expression within cells and tissue. And since the 2016 inception of the project, we’ve now been able to take bench science and automate bench science to the point where we can now run hundreds of samples, looking at thousands of genes in a fully automated process. So you’re building on this existing technique of single Mol, single molecule RNA for essence. Right. And so, and this is, I don’t. No, it’s nothing new, right?

This is a technique that’s been out there. I guess the question is, is what are the fundamental advances that resolve is bringing to the table or your version of. This, that that is uniquely powerful. Yeah. So so as you said, our technology is what’s generally referred to as a single molecule FISH technology, fluorescent insight to hybridization, which means we label RNA with a four or four, and then we can image that RNA using high power optics.

And so there are numerous approaches to look at labeling RNA and there are numerous challenges in doing that. We have come up with a novel. And of course, because we’re a biotech company, a patented process,

Harry Glorikian: Ha ha.

Jason Gammack: We have a process that allows us to through combinatorial. Labeling of the RNA allows us to identify very diverse RNA. Because the challenge is, is that when you want to label something, you have to attach a protein to, and then the genome or the transcriptome, there’s a lot of repetitive sequences that are similar and you need to be able to discern the difference between GNA and GB.

And they could be very, very homologous or very, very similar. Our technology allows us to use small, but very different probes to tile across that, that target, that RNA of interest. And then by selectively colorizing and D colorizing, those proteins, we create an essence, a color pattern. And that color modernize image, and then we’ll use software to deconvolute or decode those images.

So we can then see individual transcripts within the cell.

Harry Glorikian: It’s funny. I feel like my, you know, history has a way of building on itself. I mean, I remember when we were doing DNA in situ hybridization and trying to convince people that this was going to be something and then. You know, molecular barcodes when I was at Applied Biosystems. So this is the culmination in a, in a sense, an advancement, obviously because of software and imaging and those sorts of things of this next stage of where this technology is taking us.

Jason Gammack: Indeed, indeed. I think that’s a great analogy. Right? Great example. And you see this kind of. You know, you see this, this trajectory of single cell biology and, you know, transcript elements is a great example of that.

You know, we started with RNA, RNA, blondes doing what’s called a Northern Burlington, you know, in grad school, we’re doing Northern blots where we need to use it. The RNA within the Northern block, I still have all my fingers, even with all the isotope I used in grad school. And so you’ve gone from very crude techniques to a much more refined technique and Illumina through their next generation sequencing brought on an amazing technology called RNAseq or RNAC.

Of course RNAC, kind of back to your earlier analogy is you grind everything up and then you read all of the transcripts. The problem is, is you don’t know what transcript came from. Like you know, you just got this huge mess of transcripts and you’ve got to kind of say, well, this is a transcript that’s associated with this gene and that genes associated with this kind of cell type.

And then a couple of years ago, a company called 10X genomics came up to take single cells. So instead of had that, say that fruit smoothie with everything ground up, they took the piece of the fruit and just kind of laid them out of the line and what they oxalated the cells into a droplet. And then did the sequencing reaction in that droplet.

You still have a kind of a mixed population there. And then through software, they would separate out the different stuff. We now take that to the next, next level where we just look at the fruit salad instead of that food smoothie or the windup fruits. We can now look at the fruit salad and we can say, Oh yeah, cantaloupe was touching an Apple, which is touching, you know, orange and orange are next to each other.

The fruit salad falls apart really quickly. Going back to the analogy of breast cancer. When we have these interactions, these patients don’t survive. So maybe we need to look differently at the drug that’s targeting that interaction. So that’s how we want to think about these problems. Now we can move them forward.

Harry Glorikian: Well, like you said, I mean, context matters. Location matters, right? As, as a guy who’s got IP and location-based services, location is a big deal, right? People don’t realize everything revolves around location. At some point. And having context to, it really adds another dimensionality of information that all of a sudden your eyes open up to what could be going on or why something matters for sure.

Jason Gammack: And this is, I mean, and again, I keep going back to the oncology use case, but you know, oncology is a blight that is all over the world and affects all human beings at some point. And the concept, you know, a tumor is not a homogeneous massive cells. You know, tumors are heterogeneous. The cells that are in the interior of the tumor are different than the cells on the exterior of the tumor, the blood vessels that innovate the tumor look different than blood vessels that are adjacent to the tumor.

And we call this the tumor microenvironment. What is going on inside that too? And, you know, coming up with a drug that can just permeate the tumor and kill it from the inside outage, whether it’s hypoxia and you started of, of oxygen. So it can no longer grow or maybe encapsulating the tumor. So they can’t grow and dies outside in.

We just don’t have a lot of visibility right now to the genetics that’s happening within that micro environment. And this is an area where molecular cartography just shines a spotlight onto that tumor microenvironment.

Harry Glorikian: Well, I’m also thinking, as you get to know these different cell types in the call, it the color pattern that they’re giving, you can almost create a fingerprint.

Jason Gammack: Absolutely. Yep. And this is the thing about the molecular cartography platform. I mean when you think about kind of science and you look at the different areas of science on one side of the spectrum, you have the basic science research. This is the hypothesis formation. You just don’t know what’s going on and you have to do experiments and you’re continuing to refine and develop a hypothesis on the opposite side of that.

Spectrum is clinical testing. When you’re looking for a yes, no, almost a binary type of answer. Right? And the stops between, there are areas such as translational research where you take your hypothesis and you refine it to a use case that’s specific to a disease. Right. And then from your translational research, you move to clinical research where you’re really applying the hypothesis of large populations.

Harry Glorikian: Yeah. But, but let’s, let’s let’s and maybe agree to disagree or just agree. But I remember that taking. Dog years, like in, in the old days. Right. And I feel like because of innovation, because of being able to do the analytics on technology, you know, on the, on the data that time is almost collapsing in on itself.

Jason Gammack: It is.

Harry Glorikian: You know there are advancements that seem to be, I’m having trouble keeping up with the literature.

Jason Gammack: For sure. There’s no question about it. There’s no question about it. The rate of sensitivity innovation, you know, it’s like Moore’s law backwards, right? And mean just kind of continue to, just to, you know, keep, keep accelerating, accelerating, accelerating, and you know, tools.

Again, going back to the next generation sequencing has provided so much data that we’re still behind when the data backlog and understanding what exactly these data are going to say, but, you know, the iterative cycles are becoming faster cycles. As new tools come online. You can really test them and tweak and adjust your hypothesis at a scale that you haven’t been able to do before.

But at the end of the day, you still have to get a patient population and you have to get a patient population that all exhibits the same thing the time. Right. So there still is massive inefficiency within, within the discovery special drug discovery process. Technologies like molecular cartography can help again, collapse some of those inefficiencies as well.

Harry Glorikian: Yeah. I mean, but if you think about like you know, at JP Morgan, they announced a Illumina announced, like we’re going to take sequencing down to $60 is our goal, like at 60 bucks, it’s a rounding her, like, why wouldn’t you. Why wouldn’t everybody like if you had, yeah.

Jason Gammack: So Elaine Mardis, who is a true thought leader in the world of genomics, she previously was at Wash U. Really at the tip of the spear in cancer genetics. She said a statement once like, I still remember it, it makes me smile, you know, it might be the thousand dollar genome, but it’s the a hundred thousand dollar analysis of that genome. Right. And so, so like we can…

Harry Glorikian: I’m just looking at so many things right now from an analytics perspective that are even making that easier.

Jason Gammack: Sure, no question. I mean, again, the machine learning is helping us sift through reams of data, understand what’s not important and what is important. And with all of the data that’s being generated, you have huge training sets, right? Massive training sets algorithms. And you’ve seen success in a lot of, a lot of areas. You know, look at companies like Flatiron and look at companies like Foundation Medicine, right. You know, I think that, that, you know, Foundation Medicine is a brilliant example of a big data analytics company, masquerading as an asset company, right?

Harry Glorikian: Yeah. I mean, and it’s the same thing I was talking to Joel Dudley over at Tempus and, you know, they’re planning on being, not just having the most information across different methodologies, right. Transcriptome, methylation, et cetera, from every single sample. But yeah. They’re also creating the piping to be the AWS so that why would you go any place else, but their platform. So they’re not just giving you an answer. They’re giving you a whole infrastructure, which is that doesn’t sound like a typical biological company. It sounds like a tech company to a certain degree.

Jason Gammack: Well, I mean, you know, the lines blur very, very quickly. You know, I look at, at what we are doing at Resolve Bio-sciences and I have as many computational scientists, informaticians bioinformaticians as I do wet lab biologists, because you use the overused analogy, you know, data’s the new goal, right? Maybe they’re able to dig in and understand what’s going on, but we need to also help our customers understand these complex data sets that we generate.

Harry Glorikian: Yeah, go and try and explain that to all of our brethren. Jason, come on. I mean I mean, I was, I was on a call, you know, last night where, you know, everybody’s deep into the biology. I’m like, I think you guys are missing this other thing. That’s moving like a freight train, right. That that’s changing. And the interesting part is, is when I’m interviewing people, is the data is highlighting some things where even the world expert goes, yeah, I would have never thought about that. I would’ve never looked at it that way had it not been highlighted to me by this system.

Jason Gammack: Indeed. Indeed, indeed. You know, you’re, you’re describing you know, I love. When my customers see the data for the first time that comes off the molecular cartography platform. I really like to be with them. Unfortunately, coronavirus today, being with teams by prefer to be at home.

Because most, everybody has a very, very similar response where you watch them and they have a hypothesis in their head and they’re looking for the data that will be the hypothesis, right. Go to the image. You can see them scan the image, looking for something, and then almost uniformly. I hear this “huh.” Just that little breath as they breathe in.

And they’re just like, Oh my gosh, there’s an answer. And then we showed them some bioinformatic tools to start looking at the day, then in a different way. And then you see that kind of sit back and go, I get it. I get it.

Harry Glorikian: Well, that’s what I mean. It’s funny because I was trying to write this book and I think I’m going to have to leave it to the, to the next one you know, before this third one or after this third one comes out is I think the whole paradigm because of the analytics we can do is being shifted in the reverse. In other words, it’s almost like the machine should present something that then you can figure out where you should develop your hypothesis as opposed to develop the hypothesis, because there’s just too much data to analyze. Right.

Jason Gammack: I’m just smiling because I have, so I think about developing software and I’ve been developing software in life science for much of my career.

You know, there’s a couple of pillars that are important in my view, in software development and the features you bring into software. One of those pillars is transparency. You know, black boxes don’t go far. And science scientists by definition are technologists. They want to understand the knobs and the dials that are under the hood, less than themselves, the advanced ones want to be.

They just want to understand, you know, where are the limits? What are you calling? What are you not competent? But the other element, and this is an element where I think the industry has largely missed and you’re hitting on Mike here is the concept of guiding. The concept of guiding the customer to insights and outcomes.

And even if you’re wrong and your guidance, you’re stimulating that scientist to think because of that, that scientists may not have thought about that hypothesis or that answer. And so by proposing the next step by proposing how that hypothesis could be tweaked, you’re stimulating thought that may not have previously existed.

And I find this to be a very, very powerful tool. And this is where, you know, tools like artificial intelligence and machine learning are critically important because you need those non-biased systems to come in and start looking at the data and making calls. And then you use your bias system, the gray matter to judge those calls and challenge your thoughts.

Harry Glorikian: Well , yeah, but that’s not the way that we’re taught. Right. We’re supposed to go in with the answer, go in with the miraculous hypothesis of this is absolutely going to change. And I just find, you know, predicting the weather. I mean, there’s just too many factors for any one human being to go like, you know, that’s the trigger.

Jason Gammack: Absolutely.

Harry Glorikian: So let’s get back to the, to the technology, like your technology. Like I think I remember reading, it’s like a 0.27 micron resolution, which I think is if I read correctly 10 times higher than some of the competitors. How do you, how do you you can’t tell me the secret sauce, but how do you get to that sort of resolution? It’s gotta be a combination of hardware and software to a certain point.

Jason Gammack: I mean, our, our resolution limit is the diffraction limit of light and the diffraction limit of light being again, we image individual RNA transfers. You know, these are very, very small, couple of hundred base pair, a couple hundred nucleotide pieces of genetic material.

And so our resolution allows us to discriminate two dots, two different transcripts that are sitting close to each other at that 0.27 micron range, which again is the, the limit of light to be able to separate those two photons from themselves. And so we are pushing the absolute edge of optics, the ability to detect these events.

There are other techniques that we’re exploring that would allow us to even go beyond that like super resolution microscopy. But with that there’s trade offs, of course, as you zoom in, you lose larger fields of view and you got to kind of manage that. And the analogy I use is squishy squishy on the one that pops up on the other and vice versa.

Harry Glorikian: Yeah. You almost wish you could layer them on top of each other and create the zoom we were talking about with the Google map.

Jason Gammack: Yeah, I mean, so, in essence,, that’s what we do. So we, we take a slice or cells that are on a slide you know, and we image through that individual cell later. And we stopped at a very, very fine fence license.

So, you know, when you fix the tissues of the slide, you’re looking at micron thick tissue stacks on top of that. So yeah, you can, then you can actually see when we image the top of the face explore and kind of like, as you think about a basketball, right, as you slice through the basketball and we see the dock, when it’s really small, we see the doc, if it gets larger than it meets its maximum, that goes back down to it’s intimidating.

Harry Glorikian: I always have a vision. When I, when I talk to people about these technologies that sort of create the maps is you know, wearing a VR lens and being able to like, look at it spatially, which would be I’ve. I’ve tried to encourage a couple of other people and some of the companies, you need to have some of your cause you might see something through that then you might not see through a normal methodology

Jason Gammack: There’s no question about it. And the other thing that we need to keep in mind and, you know, as a 50 year old scientist, it’s difficult to always think about who my customers are, which are non 50 year old scientist or the postdocs and grad students that are going to become the next leaders in science.

You know, everybody talks about digitization, you know, that’s kind of granted that things are moving to digital. But we can’t ignore macro trends such as augmented reality and virtual. Right. That’s even, even me being a dinosaur, I’ve got an Oculus, of course I have a nine year old and 11 year old as well.

I, at one point in my career, I worked for a company called Sigma Ulrich. Cigna is now owned by Merck. But Sigma Ulrich was a leading company in fine organics for industry, you know, high throughput, you know, synthesis of, of pharmaceutical compounds. And 20 years ago, you would walk into the chem informatics suite and you’d see people with these huge honk and goggles on, as they’re looking at structure function, relationships, they’ve got molecules.

How do you dock molecules on the proteins. Biology surprisingly hasn’t kept up, you know, how many biological tools are using augmented reality, virtual reality, right?

Harry Glorikian: No, I know. I mean, I’ve been, I’ve been attending and going to different talks from the tech world, right? The entertainment world. Right. And looking at the boundaries they’re pushing, and then imagining that in our world, the opportunities are mindblowing.

Jason Gammack: They are.

Harry Glorikian: It’s just our world doesn’t think about it that way.

Jason Gammack: But when we think about again, the molecular cartography platform, so, you know, why did we call them molecular cartographer? Right? The cartographers were the explorers of the new world. Yeah, they were the folks that went out and map the world so everybody could follow behind and find the riches, the land, the bounty, and so on.

So when we think about how we want to build a map, if we really think about building a map for a single person, we’re losing that race and tools like augmented reality and virtual reality have future in our technology. And Harry, and I see a day and not far away where not only will we be able to look at these beautiful images that we create in this three dimensional space where you can sit, put your goggles on and look around at your Sunstone, your restructuring yourselves, and see the transcripts.

But more importantly, my collaborator in Zurich can join me on that journey. And we can collaborate, you know, virtually, but yet looking at a actual scientific experiment underway, you then take that to the next level and get into therapeutic approaches or clinical approaches where a pathologist and a general practitioner can explore the tumor biology of the patient.

It is a complete paradigm destroying proposition.

Harry Glorikian: Well, I’m also, I’m just thinking about man, if you put that into the education system in a different way to have people look at this, right. As well as super-imposed tools from, you know, the artificial intelligence world to sort of highlight different things that the machine might be able to, that that now you’re talking about really seeing where you could drive diagnosis treatment, therapy, you know making new drugs or for that matter. I mean, you know, we have these big projects. I talk about thetranscriptome and the genome, but we should have one around this cartography area, although I I’m sort of struggling to figure out whether how consistent the map would be.

Jason Gammack: Well, I mean, so the point is we build maps of every tissue type and every disease state.

And this is where, again, the ability to harm it’s software to help us interpret those maps is going to be critically important. So one element where we use software in our, in our workflow and machine learning is in identifying cell types. And so, you know, most neurons look the same or have a similar phenotype to those neurons.

Yeah, right now there’s inefficiency in a lot of biology because in essence, we use channels to identify a cell type and that channel was then occupied, identifying the cell type. What if we could free that channel out to identify more, say disease, specific genes. And so to do that, we need to still be alive, identify the cell type.

So we need to train algorithms to be able to look at tens of thousands, millions of slices of brain. To be able to identify the neurons, the different cells within the brain, so that when we put it into a wind storm, we don’t have to use a channel to identify a neuron. We use all of our channels to identify disease, state genes, and then we use machine learning and envision learning to be able to overlay, okay, that’s a neuron because it looks like this and we’ve got 57,000 data sets that support.

Harry Glorikian: It feels like facial recognition in a crowd.

Jason Gammack: Yeah. You know, it’s it, it is. And then we take it to another level when we now start phenotyping disease States. So, you know, we’ve just finished an early access program with the molecular cartography platform. And we looked at, you know, a number of different disease States.

One of them being Alzheimer’s disease, that’s a disease my grandmother passed away from. And I’ll tell you, I think most people listening to the podcast. I’ve had someone in their life who impacted by Alzheimer’s disease devastating disease that steals the person in front of you. And, you know, we have been able to make mouse models that have, you know, 10 cow tangles and amyloid plaques, and we can demonstrate Alzheimer’s disease, but yet, you know, as well as I nearly every company that’s been in the phase three clinical trial for Alzheimer’s drug has failed.

Harry Glorikian: Yes.

Jason Gammack: You have to ask why is that happen? Right? What are we missing? Even within those trials, people are looking at different approaches to address that. And so we partnered with a major pharma company to use our technology, to look at amyloid plaques in a way they haven’t been able to do before to look at an amyloid plaque. And then as a, as a temporal spatial approach, being able to identify a plaque and look at the cascading impacts of different genes that are expressed in proximity to the plaque itself.

To say, you know, right now we have been focused on the plaque. Well, let’s take that spirit further and let’s focus on the micro environment around the plant and understand what is causing the plaque to grow. What are elements, what are genes that are in play that we could potentially target from the therapeutic area that we see high levels of expression.

What happens if we turn that expression down? Can we get that plaque to stop growing. More importantly, it couldn’t get that plaque to actually shrink in size. And so a lot of these really interesting questions that previously were difficult to ask and answer our cartography platform is now allowing some unique insights.

And so it’s a great study. We’re writing a manuscript right now, and I look forward to being back on the podcast talking about, so.

Harry Glorikian: That’ll be great. I mean, I, you know, I, I have talked to some of those companies and I think one of the biggest problems is. You know, the guy that looks at images is used to looking at images, the person that works in the assets, it’s hard to get them to come into a room. And I, and I’ve seen them in a room. They still don’t do the interactive discussion. Right. They don’t, they’re not using the machine learning platforms that I’ve seen to really bring together the understanding, which would then go to being able to segment the population. Because I think half the failures are we might not be subsegment thinking the population in the right ways.

Jason Gammack: I think that’s spot on. I mean, the ability to phenotype the population appropriately because of phenotype is still usually determined by a person, you know, and that’s a physician well-trained, but yet there’s nuance and especially in diseases.

Like Alzheimer’s that are highly nuanced diseases in different States. And so I agree, and I made the comment earlier about, you still have to get the patient population to study and you have to make sure you can properly identify that population.

Harry Glorikian: So let let’s jump back here and switch to a different gear that the story of resolve the story of Qiagen, your personal story They’re somehow all. Intertwined. I feel like we know a lot of the same people that caused this intertwining to happen, but, but you know, how, how did you between the startup and you becoming CEO because you were an instructor and I think that was a pretty good gig. So how did this, how did this come come about?

Jason Gammack: Yeah, no. So it’s a great question. So, you know, again, I was at Inscripta, it’s a fantastic company and just amazingly talented people working on some really cool technology that is going to drive sustainability in a way. And so for me to leave that, obviously we have a pretty compelling opportunity here. And this story started back in 2016 at Qiagen, when we were looking at trying to come up with some really unique science to solve this spatial challenge. We brought together a team of brilliant scientists to in essence, their only job was to figure out how do we create tools that really at this phase spatial context that started in 2016, we worked together as a team to develop that technology.

I stepped away for two years to go to Boulder, Colorado, and stand up and sprint. Back in 2020, a pair of shots, the former CEO of Cajun and Michael, the founder said we got a union, got opportunity to Jason to build something really special. And, you know, it was one of those things area where I remember, well, of course we were all locked in our basements during the 2020 time.

And I remember having a conversation with parents walking upstairs to to talk to my wife, Adeline. I said, I think we’re moving back to Germany, I think.

Harry Glorikian: And she said?

Jason Gammack: And she said, hell yeah, let’s make that happen. And so it’s you know, Germany is a very special place for, for my family. You know, we lived here for five years. The first time my children moved to Germany. We made the choice to live in Germany, like a German. We have amazing friends here and our children went to school, a great school here, public schools, and speak German like native Germans. Yeah, we really discovered the heart of a, an amazing country and just gracious people and great scientists. You know, we’re starting something unique here. There aren’t, there’s a lot of startups in Germany. The German startup culture is a very different culture than in the United States.

And as I say about a lot of things, If we could meet halfway and be the perfect world, you know, to give you an example of when we’re raising money for Resolve, we’d speak to American investors and it would be don’t. You need more money. And we’d speak to European investors and they’d say, why do you need so much?

So if you could meet halfway, sometimes the overexuberance of just throwing money at problems versus the conservative. Well, you know, let’s do this incrementally and so on. You know, when we started Resolve, we had a choice to make doing, bring the business to the United States or do we grow the business in Germany.

And we had a lot of discussion around that. And you know, for me, it was a very obvious answer. The answer is we take advantage of both worlds. So in resolved bio-sciences our corporate headquarters is in Germany and our product development center of excellence is in Germany because it’s thinking about what our core technology is.

It’s molecular biology cooked to automation and engineering with optics and software. So I think we can all agree that the best physicists and optical engineers in the world reside within 500 kilometers of where we are right now, here in Dusseldorf, Germany, just amazing talent and companies that have created huge industries, such as CISE and Leica and so on are all based in Germany. Right? And that goes to, you know, the German engineering, German physics optics itself. Great molecular biologists. We’ve got amazing academic centers across Europe and bull, so on and so forth that develop amazing molecular biologists. And when it comes to our computational abilities, that’s a global skillset.

I’ve got a great development in Eastern Europe. I’ve got great developers in Western Europe and great developers in the United States. We’re opening our office in the United States and San Jose, California, and the Bay area. And one area where the us has excelled past Europe is the softer side of science.

So the marketing, the commercialization, the brand development. So we’re going to put our feet on both continents and really use those pillars of excellence. North America will be our commercial headquarters of our business, where our marketing and brand creation, outbound marketing content creation efforts are going to reside.

And Europe will be our center of excellence for product innovation and product development. And so we’re going to really be able to harness both, you know, amazing capabilities that each region brings to us.

Harry Glorikian: Yeah. I, you know, whenever I’m talking to different companies and they’re talking about where they’re going to be geographically, I mean that, that people, people don’t give that enough thought as much as they, I think they should, because there are cultural differences and that. Can really hurt you if you don’t understand these little nuances. I mean, I can tell you the difference between being in Canada and being here big difference. Right. And people say, well, no, but it’s right there. No, it’s actually not right there. It might as well be in a different place.

Jason Gammack: Yeah. You can work straight. Also the difference between being in Southern California in Northern Colorado. But it’s very, very different. I’ve lived in San Diego and in the Bay area multiple times. And the difference between the regions are, this is significant. Yeah, no, I grew up, grew up in Northern California. And when I would say to someone, I was from California and they’d be like, Oh, you’re from Southern California.

I remember being like, no, absolutely not. Don’t don’t tell me that. Cause you know, you didn’t Northern California had more of a. Well, when I was growing up a relaxed, you know, yet, you know, we want it to be ahead at least from an intellectual perspective, but. And now the Northern California has gotten a little arrogant thanks to tech, but you know, it is what it is.

It’s driven a just unbelievable amount of growth that tech has and unbelievable amounts of innovation has come from that region, which is why, you know, when we looked at. Where we wanted to open our us office. We were eventually the two narratives. We looked at Boston, Cambridge, and we looked at the mayor.

I mean, those were the two areas that we honed in on and we made the decision to be in the San Jose, San Francisco area. You know, we know the market well, talent is amazing there. You know, Stanford, Berkeley, the universities there just contributed just an amazing amount of, of gifted computer scientists and developers and so on.

You know, both cities would have been great. But California is where we will have our us operations. Well, when do you expect that to open? We hope to have that opening in April. That’s our, that’s our plan.

Harry Glorikian: When do you guys launch, when is this gonna…

Jason Gammack: Yeah. So, so, you know, within the life science tool space, there’s a very say kind of common dissemination path for, for technology.

So technology like ours, which is very complex and capital intensive. It starts with the company, refining that technology and then gain granted access to that technology too early access customers, usually key opinion leaders or thought leaders in particular fields. So we have just completed our early access program, or we had 15 institutions involved in that program.

The focus of that program is really to understand the. Application space and how our customers are thinking about using the technology. The technology that point has exited product development. So we’re not really still developing the product, finding and nudging and guiding the product in areas like software, or you never stopped developing software software where it’s just a constant development.

You know, we put a flag in the sand and say, this is where, what the software is going to start. And we do a lot of user acceptance testing and understand how the customers are going to use the software and then start dropping those features that we want to incorporate. Once you finished early access, usually what you then move to a dissemination approach, which is what we’re in right now.

And so for us, dissemination is twofold. Our product is largely data. I mean, that is our product. You know, a random molecular cartography generates four terabytes of data, which is a significant amount of data. And so we are launching a data as a service approach where we will run molecular cartography and our service lab had spoken in our North American facility expanding our European facility.

And at the end of this year, our plan was to open a facility in Asia. So we can begin pushing our data to market because especially when it comes to things like software, we will never develop faster than the community will develop. And quite honestly, the community is going to bring ideas to us that we’ve never even thought of before, how to look at the data.

So we are going to scale our services to provide more access to the technology. Early access is tough because you have to say no to customers. You have to say, yeah, we’re oversubscribed. We can’t take you in. We’re not going to open up the phone with the brain. The second phone number dissemination strategy is we have a number of large advanced institutions that want the workflow deployed at their facilities.

So major pharma that sees this as an amazing insight and a biomarker discovery and understanding, you know, how do they move the ball forward, even faster? Talk about collapsing those cycles. So we will be in the latter half of this year, deploying the technology at very advanced, very qualified customer sites.

And then the last phase of dissemination is what I call the democratization phase, which is when we then kind of push the button and start pushing the platform onto benchtops. So it scientists at university scientists and non-profit research institutions and so on. And that will happen in, in the later months.

Harry Glorikian: But you almost wished like… I’ve become a believer. And I know that this is, you know, sometimes it’s a pipe dream, but you’d want this, all these images, like Google maps to at some point coalesce into one repository. Like I understand that everybody wants their own confidential information, but. We didn’t build the human genome on confidential information. We, we sort of put it together and said, here’s the genome, right? Otherwise, nothing we have right now would have, you know, been realized and everything is built on that, on what was done in those early years. I feel like what you’re doing almost. If you’re going to build a map, you need everybody mapping. And adding to the map so that everybody can then benefit from it in their own unique way.

Jason Gammack: No question about it, you know, you and I are in violent agreement on that point. And so hence our urgency to get our data into the scientist’s hands so that they can understand the value and the number of insights that come from the data.

So there are a number of international consortium efforts on your way right now that are commonly referred to as cell Atlas efforts where they’re is different cells. And so on. We want to put the cell Atlas three-dimensional context and you know, those are a couple of stories. And so, so we have a strategy to engage those organizations to be able to kind of say, okay, you’re now not in the single cell sequencing.

You’re done single cell RNA seq now we need to take it to the next level, take that RNA seek data, which is the counting of the transcripts in a tune D kind of planar effect. Let’s now blow that into a 3d effect. Let’s correlate our visualization of the transcripts with the digital readouts of RNAC and this collaboration that I spoke of with this major pharma company in Alzheimer’s.

We did it in their Alzheimer’s mouse mall. Where we correlated all of the single cell on a sick day that they’d been accumulated over the last five years and map that to three-dimensional spatial, single molecule fish data. And it was a beautiful study because we showed a correlation and R squared of 0.9, nine, seven to the RNA seek data to our visualization of the transcripts.

And then we added the three-dimensional context, very importantly, at some cellular resolution where you can actually see structures within the cells. And so it was just this. Yeah, it was one of those kinds of moments where you get goosebumps and you’re like, Holy smokes. This is real. I mean, we knew it was good, but this really showed how good it was.

Harry Glorikian: Well, I’ll look forward to that to that paper when you said it’s, it’s on its way for publication?

Jason Gammack: We’re reviewing the manuscript now. So it’s an iterative process and it’s a major pharma. So, you know, they’re embargo mania.

Harry Glorikian: Well, when it’s out, you can, you can send me a copy, but Jason, it’s been great to talk to you. I feel like we could talk. Knowing the last time we talked, we could probably talk for hours about these things. But I I’m sure that you’ll, we’ll have you back on the show when we get to the next iteration. You know, what we should do is we should, we should get Per to come on the show with us and, and, and do a three-way conversation because his perspectives are always insightful and unique.

Jason Gammack: Indeed. He is a I’ve known Per for 20 years and the opportunity to join with pair and start this company. It was an amazing opportunity. Truly a thought leader and a visionary in the field. And we just had so much runway in front of us. We’ve got such an amazing team and the team is growing amazingly fast and it is truly an honor and a privilege to be working with them and bring this technology to market because we believe that this technology will absolutely have a positive impact on the human condition. There’s no question about that.

Harry Glorikian: Well, you know, I just, like I said, I’m reflecting on, you know, the, what immunohistochemistry opened up to us. And I still don’t think it gets the credit that it deserves. Right. But I think now with the computational capabilities and the insights that that could provide, and then you can overlay other information onto that it’s changing the con the context where the persistent identifier is the location, but then everything that’s happening around it is what really puts it into context of what’s happening in that cellular dynamic. So great talking to you and I look forward to keeping in touch.

Jason Gammack: Absolutely. Thank you, Harry. Really appreciate it.

Harry Glorikian: That’s it for this week’s show. We’ve made more than 50 episodes of MoneyBall Medicine, and you can find all of them at glorikian.com under the tab “Podcast.” You can follow me on Twitter at hglorikian. If you like the show, please do us a favor and leave a rating and review at Apple Podcasts. Thanks, and we’ll be back soon with our next interview

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