Proteomic Profiling Platforms

Welcome to the Proteomics in Proximity podcast, where your co hosts, Dale Yuzuki, Cindy Lawley and Sarantis Clamydas from Olink Proteomics talk about the intersection of proteomics with genomics for drug target discovery to the application of proteomics to reveal disease biomarkers and current trends in using proteomics to unlock biological mechanisms. Here we have your hosts Dale, Cindy, and Sarantis. Welcome to the Proteomics and Proximity podcast. This is your host, Dale Yuzuki, along with Cindy Lawley and Sarantis Clamydas. We want to welcome you today. We've got some pretty exciting news, and by news, I mean a recent paper. And Cindy, would you like to introduce it? Sure. So this is a, uh, paper that came from Daniel Katz and Rob Gertszten and a series, uh, of co authors as well. It's in Science Advances, so very, uh, prestigious journal. And we've known that the work had been done for quite a while and we knew that it was going through peer review. And so we've just been very excited to see it come out and we're excited to talk about it today. Uh, go ahead. Yeah, sure. It's a comparison paper among proteomic profiling platforms. It looks at antibody, uh, methods versus aptamer methods. The pros and cons pluses and minuses. I think that, uh, both platforms are incredibly valuable. So the two platforms that were compared to the SomaLogic platform, their also their 5K. Uh, and the OLink platform, this was our previous product, the Explorer which uh, they called the Olink 1.5K So yeah. Dale, do you want to give us a little bit of background on Soma? Sure, happy to. I was a protein product manager for QIAGEN back in the early 2000's. And this was maybe Protein Science Conference and I met a very interesting individual named Larry Gold. He was the founder of SomaLogic, a company in Colorado. He's a genius, really. And he says, yeah, he's a genius. A uh, remarkable individual. He made quite an impression on me because he was working on a very unusual approach to protein detection. Instead of using, uh, recombinant antigens and inoculating goat or mouse or rat to develop antibodies right. Or develop hybridomas or monoclonals, he actually was using a method of in vitro selection. it's in organisms like microorganisms, a series of selective processes, to find out particular synthetic nucleotides, either RNA or DNA, that would bind very specifically to proteins. This is called aptamers. These are synthetic stretches of DNA and RNA that bind to proteins, you might say. Well, we have DNA binding proteins, RNA binding proteins all the time. Well, yeah, transcription factors, what have you. But these are proteins that have been evolved to bind to DNA sequence. Now we're doing the reverse. We're actually finding out what sequence can bind to a particular protein that doesn't normally bind to DNA. Well, they've evolved, the technology evolved, uh, not only to use natural oligonucleotides, they actually have, uh, developed something called Somamers, which are unnatural nucleotides, uh, because if you think about the positive charges of DNA, etc, etc, etc, etc. All you're going to have certain limitations in terms of how the DNA and RNA can fold, using natural nucleotides. So they've developed some chemicals they call Somamers to expand the kind of, uh, synthetic aptamers, what it's called, to bind to more and more proteins. So they've scaled the technology in earlier. They published some really high profile papers. And what these papers did was look at 1300 different proteins out of the circulation and connect it to the genomics. So what they would do is, frankly, since you had a million genotypes from, um, an Infinium array, a genotyping array, and you have all these genotypes from individuals, they can do GWAS to protein level using the SomaLogic, um, readout. Again, they're doing GWAS to the circulating protein level and then connect that to phenotypes. And the first high-impact papers in Nature and in Science were pretty remarkable because you're talking about genomics and you're tying it in to the circulating proteome, which you then can tie into disease. Yeah. And I'll ask, I'll also add is, you know prevailing technologies and mass spectrometry. Of course, looking at proteomics, um, amazing advances there and a lot of, um, transition to the clinic, some of the discoveries there. The idea of being able to, um, hook out of, um, a plasma sample, for example, uh, to hook with an affinity-based method like a Somamer allows uh, you to do the low abundant proteins, the ones that just may not show up very often or in very high abundance in plasma allows you to start to look at patterns of those proteins as well with health and disease. And so I think this is amazing innovation is that in Mass Spec, of course you can do this, but it takes a lot more sample. And doing it in large numbers of samples can be challenging simply because of, uh, what it takes to put a service wrapper around running many samples through a mass spec. Uh, I think you bring up a really good point, Cindy, which is people just say, well, why can't you just use mass spec? It's a mature technology, it's been around a long time. HDL, LDL, we've got a lot of great clinical labs from it. Exactly. But it's the level of abundance, right, in that there's a number of really important molecules that aren't very prevalent in the circulation that both, uh, the Somamer technology and the Olink technology can pick up that Mass Spec simply cannot. You also bring up the other point, which is Mass Spec, uh, has a lot of, uh, sort of upfront steps, right? If you're doing liquid chromatography, tandem mass spec, there's a whole bunch of sequential... steps uh, you have to do that is just not high throughput is that correct? Yes. And managing the variability. Yeah. Sarantis? Sorry. Go ahead, please. No, I'm sorry, I was talking about Mass Spec and plasma. We know that in plasma there are like 40 or 50 proteins that are super abundant. And when you try to run a Mass Spec, you let's say mask all the other proteins. And for this is for uh, low abundant proteins. I think affinity, capture assays, like Olink assays, can help to identify this, because you overcome this problem with Mass Spec. It's a nice complementary approach, obviously. Yeah. Sarantis, it would be great if people could get kind of an overview of how Olink is different from the SomaLogic approach, because of the two platforms being compared in this paper. Would you mind tackling that? Yeah, I mean, in this paper, actually, they have uh, used Olink Explore our first Explore based on the NGS platform. And SomaScan That's the most expanded version, with more, let's say, reagents. And they profile Jackson Heart study, around 500 people, and Heritage Family Study Uh, in the first case, when they see, they try to see overlap in between. SomaScan 1.3K and Olink they see like roughly 500 proteins being overlapped. When uh, they switch to the expanded version of SomaScan, they were able to see like uh, actually proteins. The nice advantage of Olink, of course, is uh, NGS based approach and uh, the antibody capturing that gives obviously more specificity compared to others. But... yes, please. So if I understand correctly, you mentioned the Jackson Heart Study of what, some Yeah, uh, 500 individuals, yes. And I have to say here, for all of these individuals, they have like whole genome sequencing data. Yeah. Mmm hmm, so really nice genetic background information. As far as a comparison study. Right. Actually, uh, before we talk about the study itself, Cindy, did you do some research on Jackson Heart? Can you tell me a little bit more about that? Yeah, so Jackson Heart Study is a community-based, it's a longitudinal cohort study. So uh, essentially they're looking at understanding, cardiovascular disease primarily, but also renal respiratory diseases. The nice thing. And what I love about the Jackson Heart study is that it's African Americans. So, it's really helping us get a view into proteomic variability, not just within uh, the northern European populations that have been characterized so well, genetically as well as now, um, quite a few of them have done proteomically, like the UK Biobank. But um, it allows us to get some sense of the diversity in African Diaspora. So... the Jackson and Jackson Heart study, then, is referring to Jackson, Mississippi? Is that right? That's right, exactly. The majority of people then are from Mississippi area. That's right, yeah. And when you say longitudinal, are you meaning that what they're followed up over time? Exactly. So they were recruited, and then followed, over time. The nice uh, thing about that is as people evolve, as they get older and they have health challenges, those health challenges can be better understood by looking back at samples before they had diagnosis of disease. And that's going to help us develop more preventative approaches... for diseases. Today, our healthcare system is a diagnosis-based system, not uh, only within the US. But also worldwide. Really, the funding for healthcare, revolves around diagnoses. And so this concept or this ability, and I think I would argue this might be, um, one of Larry Gold's big motivations in developing uh, of the SomaLogic technology. I think we're really excited about this for Olink as well, is the ability to be more preventative and understand risk, not only from the genetic perspective that has been enabled over the last 20 years, but also from the proteomics perspective. And so understanding proteomic risk at any given moment, um, appears to be providing a little bit more of a window into more real-time health. And so I think that's the important aspect of having longitudinal data like this, especially in this underrepresented population. So this is a pretty expensive undertaking because we're talking about whole genome sequence out of these 568 individuals. Right? And then you're also talking about, Olink 1.5, Olink Explore 1536 plus, SomaLogic 1.3K on all the same samples. Do I understand that correctly? That's right. And as far as then, what can you tell me about the Heritage study? Yeah. So this is health risk factors and exercise training and genetics. So the HERITAGE stands for literally, that: HEalth RIsk factors. exercise, Training And GEnetics. And it's, ah, um, a partnership among I think it's seven universities, actually. I don't have those seven universities. Maybe it's five universities. Sorry. Yeah, I think it's five universities and I don't have them off the top of my head, but they're across the US. As well as Canada. So, um, really trying to get information across a large segment of the population and again, whole genome sequencing information. Is that right, Sarantis? That there was whole genome sequencing within the Heritage as well? Yeah, which like you said, Dale, it blows my mind because I think about the exome um, sequencing consortium from the UK Biobank, which is a massive undertaking, but still exome sequencing is only about 1 to 3% of a whole genome sequence. So we're talking a lot of sequencing to your point. And this is really important, especially for health equity, because we really have an underrepresentation of African Diaspora in um, in sequencing data and you know being biased by a chip that might not have a lot of content that was designed, a genotyping chip, I'm thinking about the comparison between a genotyping chip versus whole genome, um, sequencing. It's sort of like getting a satellite view of a population census. You can take a picture from the satellite and you can estimate the representation in those, those buildings that are in an eight block area. For example. Or you can go house to house and you can knock on the doors of every one of those residences. Which one is going to give you a more accurate representation of the population? The knocking on the doors. But... it's going to be a lot more expensive as well. Right. So whole genome sequencing goes base to base to the extent that our sequencing methods allow that. And we're going to see diversity that we might not know about before... when we developed that genotyping chip initially. Yeah, that's such an excellent point because you just assume all, you just get the genotypes and then you capture the majority of the variation. But what you're saying is yes, the whole exome sequencing doesn't capture a fraction of that variation because right. These SNPs are in non coding regions. That's right. And to be able to get them the whole genome sequence, we can get a very fine grained look, right, at the variation within the population and then the connection to risk and disease. Is that right? Yes. And you don't know what you don't know. Right? Uh, so the fact that this is whole genome sequence, I just think Rob's team did a phenomenal job of bringing together important data to really, advance our understanding not only of the two platforms, but also of advancing health equity. Yeah. Yeah. There was something else. I'm sure it'll come up again if I think of it, but um, it's essential that we characterize diversity in these populations. Sure. So Sarantis, what can you tell me then about the primary findings? Actually, really uh, nicely, they try to see if there's a correlation with cis-pQTLs. It's something that comes again, we have seen it in other papers as well, that Olink assays based on antibodies has a nice correlation with cis-pQTLs. And actually Olink Panel is uh, associated with new pQTLs. And I think that is a really important, um, finding, especially if you want to identify new biomarkers and drug targets. Right. Do you agree? Yeah. And we have seen a lot of cohort studies actually, uh, Cindy you can have some from your side and from your experience as well on that. I was just going to define cis- pQTLs again. I know we've talked about it previously, uh, on episodes of this podcast, but as a reminder, a cis-pQTL is a correlation in genotypes at a locus, with protein levels. Again, just a correlation, but um, it's something you can detect through statistical analysis and large data sets. And of course, the larger your data set, the more your power to detect any association. Right. So a cis- pQTL is when a variant is correlated with protein levels. If that variant is within a million bases or itself that is coding for that protein, that's what we call a cis-pQTL. So it makes good logical sense, makes us feel good about the measurement of the protein. If you actually see a correlation between a region that is coding for the protein and that protein itself. The... other thing to say about that is there are good biological reasons why sometimes you might not see a cis-pQTL there's protein-protein interactions that might knock that protein level off of a, uh, correlation directly with that region. But... it is a nice feel-good measure that you're measuring the right protein when you do see a cis-pQTL association. And that's a tool that this team used. And to be absolutely clear, "cis" means it's within that one megabase... close. And P... Yes. And "p" stands for protein, and QTL stands for "Quantitative Trait Loci". So you're saying that a particular SNP genotype, the loci, is actually controlling the level of protein as a quantitative trait. I'd say appears to be controlling it. Appears to be controlling. Associated, yeah. Right. Statistically associated, yes. You can't say causality at that point where you're just looking at correlations. So we're associating the presence of a SNP within 500,000, bases of a particular gene, and that SNP is positively associated with the level of that protein in terms of Olink-quantitated or SomaLogic- quantitated understanding. Associated. Yes, that's right. Okay. And the value of these pQTLs is?... So the cis- pQTLs in this paper were used as a... a surrogate measure of specificity. But in general, cis- pQTLs as well as trans-pqtls. And those are ones that are correlation with protein level that's outside of the gene that's coding for that protein. And that means outside of that one megabase region, uh, around that gene, that those are valuable because they help us understand the pathways that may be important for diseases that are associated with not only the proteins, but also diseases that we've identified in the past through GWAS. This catalog of amazing GWAS associations, uh, it helps us understand what protein pathways are involved in those diseases. And then, of course, if we have a sense of protein pathways important in diseases, that gives us the ability to start to propose therapeutic targets or ways that we may develop therapies to go after these proteins or to go after the mRNAs that are translating to proteins. ... To then have an approach to either nudge people back into health away from disease that's that preventative side or as what our health care system pays for today. Which is drugs to treat diseases once they've been diagnosed. So here we get the payoff of the Human Genome Project right? Which is new drugs, new diagnostics, new therapies potential for cures, is that correct? Potential for cures. Right! Which is right now, what do we say, 90% of clinical trials? I think that's the latest number that I've heard. 90% of clinical trials are failing. And the ones that you have genetic information going into the clinical trial, uh, have been reported to be twice as successful, so twice as likely to be successful. So the question is, what can proteomic signatures from SomaLogic or Olink. What can these approaches do to help improve the success of clinical trials? I think that's yet to be seen, but that's certainly the hope of the future. And using leveraging large data sets like these important studies like Jackson Heart study and the Heritage Family study. Uh, Sarantis, you mentioned 40% in terms of cis-pQTLs. Sort of getting to that, right? What did you mean by that? That means that from the old thousand five hundred proteins, uh, that they check from Olink platform, more than 40%, they are correlated with new cis- pQTLs. And I think that was really amazing. That's a really amazing number, because it gives the possibility to identify new biomarkers, for example, as you mentioned before, a new drug targets. And, and the nice thing of the Olink assay is that not only, uh, were correlated proteins, they are having cis-pQTLs, but also when they don't have correlation with the Soma assay, we have, cis-pQTLs. That means they have a really nice capability in the Olink Explore to identify this cis-pQTLs. That's the take home message from this. And so what was the percentage relative to SomaLogic? I think Soma if I'm not mistaken, it's like roughly So the higher percentage than the overall numbers, were they also the overall numbers are different? Yes, of course. I think I'm just looking at the paper as you're talking, Sarantis. I think for Soma there were 370 of 1301, cis-pQTLs detected for Olink, it was 575 of 1472 total measurements, uh, where they detected cis-pQTLs. But like I said, there's good reasons why we might not sometimes detect a cis- pQTL. So I think one of the interesting aspects that I didn't see them, um, I saw a little bit of this, but the ones that they have in common between the two platforms, if you can see a cis-pQTL on one that would suggest that there should be a detectability of a cis- pQTL, then you should be seeing that on the other. And in fact, I think the comparison between the two I think the median comparison was about 41% between the two platforms. Am I remembering that right? I didn't bring that pull that figure out. Um, and so it's compelling, right, to wonder, is one platform actually pulling in a, um, phosphorylated version of the protein, as well as the protein, uh, without the phosphorylation, which may be good information to have. If you map those epitopes, then, um, you can determine that, I think. But, uh, I think that's the value of being able to look at both technologies together and the complementarity of them. And I think David, uh, does a nice job of characterizing that. And I will also point to something you showed me. I think it was you, Serantis. You showed me the tweet, um, that David put up on Twitter that has a beautiful walks us through his primary findings which maybe we can put a, ah, link to that Tweet in the show notes. I see. Yeah. You're referring to Daniel Katz, the first author. I know it's hard to visualize no worries. It's hard to visualize large numbers. Right. We're talking about roughly, what, 370 out of Um, and so, yeah, these numbers, they're hard to remember, but nonetheless, I think the take home message, right, is that when you compare both side by side on these particular platforms, sort of the findings, uh, of cis- pQTLs is really important. It can be useful as a discovery tool. The overlap is what you're saying? The overlap between the 370 and pretty low, is what you're saying. Yeah. Is that right? I think this specificity analysis, uh, that they did was, um, super important. I think another aspect where we weren't showing up as, um, beneficial, as I would love for our assay to show up, was in precision, in what they assess as precision and repeated measurements. So Sarantis, you had a really good explanation of that. Do you want to go over what they talked about in the paper? There actually, um, authors, they have seen that Olink has bigger CV's than, the Soma platform. There could be a lot of reasons, but they speculate that one explanation could come from the fact that Olink, we are using small sample volume for, our assays. Another explanation could be for the fact that Olink antibodies are polyclonals. This could affect precision, but may also make them more resistant, making them more resistant to binding interference. That means that it will capture some, uh, complexes, some protein complexes, that aptamers could not see or could not capture, because their interface are covered by proteins. So one of the advantages of using higher volumes in an assay, certainly I think, might be that your coefficient of variation are these CV's, which are surrogate measures. We've got these surrogate measures of precision, repeated measures being right spot-on top of each other. Um, that might be a good reason to have higher volumes. There are trade offs, right. They have seen also that if we pool sample plasma, then we improve our measured CVs. I have seen this happen. One of the very strengths of Olink, which is using minimal sample volumes, what Explore only needs like six microlitres is actually a weakness, which is interesting in terms of the variability. But I think we can say that the pQTL results - right - do speak for themselves. But there's another angle in the paper that I think is some of the strongest data. And this is regard to phenotypes, right? Yeah. And it speaks to just to touch back on the precision. So, in a vacuum, or when only thinking about precision alone, when you have higher CVS or higher, um, variability, you need bigger sample sizes to detect a difference between, say, cases and controls. In a biomarker study, for example, uh, and so if you're just thinking about precision in that way, it's really important um, as a consideration for power. And so then, Dale, this uh, is where the rubber hits the road, right - is trying to make a phenotypic association, in the real world with disease. Do you want to summarize that for us from the paper? The phenotypic, uh, results? Yeah, those phenotypic results were really interesting because they pulled out some eight different phenotypes from total cholesterol to EGFR to body mass index. And they show this bar chart where. The Olink pQTLs right, compared to the SomaLogic pQTLs, there's this huge difference on a phenotype by phenotype basis. And thinking about it, it's well this is really what you care about, which is phenotypic associations between the genetics and the particular thing you're measuring. If it's uh, hemoglobin A1c, if it's systolic blood pressure, these are biomarkers, these are phenotypes from the population that they really care about. Why? They even have an association with ASCVD risk score. And if you've taken a physical recently, your doctor will actually have you calculate your ASCVD risk score. And I was really surprised in my last physical where I'm punching in the numbers and they're saying, okay Dale, you've got an elevated risk of 4% and we need to keep an eye on this. Uh, but nonetheless, those phenotypes are real world, everyday rubber meets the road, like you mentioned, Cindy. Yeah, it's exciting because this is really what Larry Gold had in mind. I think this is what Ulf Lungren had in mind in terms of being able to broaden uh, a discovery platform for proteomics. Sorry, I'm sorry. Go ahead, please go. Sarantis, please. No as an example, brings up the HSP-70, it's really uh, well-known and famous heat-shock protein 70 and connected correlated with a BMI. And there are a lot of studies nowadays for, drugs against uh, the activity of this protein. Actually, that's uh, really exciting finding. And regarding heat-shock protein a handful of Elisa's at the very end of the paper. Sarantis do you want to comment on that? Yeah, I mean they try to see which of the Soma and Olink proteins correlate better uh, with uh, Eliza. Of course they use Eliza that they are, let's say, commercial available for this. They focus on these five targets, let's say. And overall it's really striking how Olink data, correlates really nicely with Eliza data. And uh, again, they focus with HSP-70 and a handful of other proteins that really nicely correlate uh, the two assays. Giving, again a bonus to Olink for specificity, I think you agree on that case. Well, and I think, I think the ability to translate to a clinical tool and to be fair, Eliza is immunoassay based, right? It's immuno-absorbent based and we're an immunoassay. Olink uses two antibodies for each protein, whereas Soma has this novel aptamer technology, this synthetic... aptamer technology, uh, that they've innovated. And so, yeah, it's something I like about antibody-based is that so many of our, uh, therapeutic targets that we use that have passed clinical trials are antibody-based. Well, thank you both for really excellent analysis of a side by side comparison paper. For those interested in the reference, this is Katz DH. This is Daniel Katz, the first author. The senior author is Rob Gerszten. The title is "Proteomic profiling platforms head to head: leveraging genetics and clinical traits to compare after an antibody based methods" Yeah. Thank you very much for joining us today. Thank you very much. "Go Beth Deaconness!" right? "Beth Israel Deaconness" Exactly. Thank you very much. Thank, uh, you thank you. Thank you. Bye bye. Thank you for listening to the Proteomics in Proximity podcast brought to you by OLink Proteomics. To contact the host or for further information, simply email info@olink.com.