Welcome to the Proteomics in Proximity podcast, where your co-hosts, Dale Yuzuki, Cindy Lawley and Sarantis Chlamydus from Olink Proteomics talk about the intersection of Proteomics with genomics for drug target discovery, 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. Hey, everyone. Welcome to Proteomics in
Proximity. Today we have a guest, Chris Whelan, who's joining us from Janssen Pharmaceuticals. Chris is the one who has helped spearhead bringing proteomics into the UK Biobank. So we're super excited to talk to him about his history, his background, and what the vision of bringing proteomics together with the genetics that UK Biobank is so famous for, the genetics and clinical data that we're all very excited about on the UK Biobank Research Analysis
platform. And this week is a pretty auspicious week because we've just heard that the first tranche of data from the UK Biobank Pharma Proteomics Project have become available through the Research Analysis platform. So we're excited to talk to Chris about that as well. So, welcome, Chris. Hey, Cindy, Dale, Sarantis. How are you all doing? Doing great. It's great to have you with us today. Welcome, Chris. So Chris, can you tell us a little bit about your background in terms of going into
science? You don't have to start sort of in your elementary school days, but certainly sort of your path to industry because I think that's always an interesting place to start. Absolutely, yeah. Happy to. So I did all of my training up to getting my PhD in Dublin, Ireland. I've been told recently that I'm losing my accent, so I'm going to try to make an effort to sound more today.
But yeah, I started off in psychology for my undergrad and then realized I wanted to get more into the cellular sort of, sort of hard science behind brain illnesses. So did my Masters and my Ph.D. in neuroscience. One of my advisors was a geneticist. So I started to dip my toes in statistical genetics. And that sort of led me towards my postdoc in Los Angeles with the ENIGMA
Consortium at USC. So there they were combining neuroimaging with GWAS, basically running, genome-wide association studies on very large collections of MRI scans. So I did that for two years. I felt that I always thought that I would be on the academic track. I remember in my PhD class, they actually wanted to do a straw poll of who wants to go to industry and who wants to be a lecturer or a professor. And I was firmly
in the professor camp. But I think two years in academia in the States, it's tough. It's tough. And I actually had a good postdoc. My P.I. was awesome. Really lovely, man. Really supportive. But it just gave me some insights into it. It's a tough place to be. Beyond that, I think I wanted to be closer to the patients. That might sound like a little bit of a cliche, but I wanted to be really working on whatever I'm doing. I can see this affecting a patient in six or seven years
time. So, I was going to move home to Dublin and then I got the call out of nowhere from Pfizer. And they were looking for somebody who had a dual neuroscience and genetics background. So it just seemed too perfect to - Ahead of your time, Chris. So when they are pulling those GWAS traits out of the imaging data, how is that being tracked? What were the connections you were looking for with the genetic data? How were they identified across different MRIs that allowed it to be compared between
cases and controls? This imaging area has evolved so much since I was in graduate school, so I'm really curious how you did that. So it's interesting, I think ENIGMA was almost like a proto-UK Biobank. I think UK Biobank was in the middle of recruitment when
ENIGMA started up. But really it was a great idea from Paul Thompson where there were a lot of different sites doing MRI scans in maybe 50 cases or 50 controls, and reporting differences in brain structure and function that sometimes were replicated and sometimes weren't. So the broad sort of oversimplified idea of ENIGMA was, well, we can't bring everything together, we can't ask everybody to just throw their data in a sensor repository. Ethics and paperwork nightmare.
But we could agree upon a standardized set of protocols to process the imaging data and ask everybody to process it in exactly the same way. And then they all send us their results because that's clean, it's anonymized, and we'll meta analyze all of our results together. So that's where the name comes from enhancing neuroimaging genetics via meta analysis. Uh, nice. Yeah, good memory. I have to type it out a lot during my post doc. That's good news, that means a lot of publications.
How did the UK Biobank come into your life? How did you make a connection with UK Biobank? And I think you have also seen all the progress, right? Definitely, yeah. It's interesting, I think that UK Biobank came into a lot of industry scientists lives around the same time. While I was at Pfizer, we were using large-scale genetic databases to make inferences around this gene is associated with this disease. Maybe it would make a good therapeutic
target. But UK Biobank came along, I guess around 2016, 2017. It really started to come onto our radar when the exome sequencing of UKB was announced. That was one of the first sort of major industry- academic collaborations where UKB worked together with Pharma to generate the biggest exome sequencing study ever conducted. That came on our radar as around 2016, 2017, I think for me personally, I moved to
Biogen in 2018. It was around the time that Pfizer pulled out of neuroscience and Biogen were all in on neuroscience. So it just seemed like the perfect place for me to work. But the first thing I was tasked with when I joined Sally John's organization was make UK Biobank useful for neuroscience, for Multiple Sclerosis and Alzheimer's disease and depression and Parkinson's, et cetera. And it
was a sort of a tall order. I mean, UK Biobank is breathtaking in terms of its depth, and it's just a beautiful, beautiful study. But it's not a disease- specific study. A lot of these diseases like Alzheimer's, they only come along when you hit your 60s. So there's not a whole lot of people in there, or there weren't, at least back when I started working with it with Alzheimer's. So there was not that many questions that we could address using UKB.
So the lowest hanging fruit for me, coming from my background with the ENIGMA Consortium, was to look at the MRI scans in UKB. Unlike ENIGMA, which was retrospective, metaanalysis, UKB are actually collecting scans across three different sites in the UK, all using the same type of scanner, the same head coil. It's all standardized. So that was the first thing I did. I did GWAS and a couple of new imaging measures from the brain scans. Things like local folding. But I felt like we could do more
to help neuroscience. And I started to play around with the idea that maybe we could look into doing neurofilament light polypeptide or neurofilament light chain in UKB. So this is like a neuronal cytoskeletal protein. And when there's some injury, when you get neuronal injury, it gets secreted into fluids. So CSF [cerebrospinal fluid] or blood. And it was proposed, it was really gaining momentum as a potential biomarker for MS [Multiple Sclerosis] and Alzheimer's and other brain
illnesses. So it just seemed like a really exciting idea. What if we could measure neurofilament across UK Biobank, across these half a million people, and we could get a database of how much brain injury do you have based on a blood sample? But quickly realized that was going to be very expensive and a little bit niche as well. There's not that many pharmas that are invested in
neuroscience these days. And we felt that if we were going to do it, we would need it to be a multi pharma consortium effort given its expense. So thought about it more and more and I had already worked with Olink on smaller scale studies. What year was this about? This was 2018, I think. 2018, 2019. But I had been working with Olink
on some smaller scale studies. I'd done some work in a Swedish neurology cohort looking into proteomic changes in Alzheimer's disease, and started to talk to Evan Mills at Olink about, "Hey, are you going to get neurofilament on Olink any day? I'd love to look at neurofilament in UK Biobank." And we started to toss around the idea that maybe, instead of just doing neurofilament in UKB, we could do Olink because it captures neurofilament and it captures many other
proteins at the same time. So we could not just make this about enhancing the value for neuroscience in UK Biobank, but just in general, enhancing the value for drug discovery and potentially opening this up to a wider consortium of pharmas. But, yeah, that's a mouthful. Basically, I can't remember the question he asked. I asked you how you got started with the UK Biobank. And it's great because you zoomed right into sort of getting 13 pharmas together. That was
no mean feat. What was it like? I mean, here it is. You're going from one protein, realizing that NFL is not going to be of general interest, and then some exposure to Olink. There must have been a lot of different conversations. Yes. I don't know where to start. If someone walks up to you today and they say, how did you do it? How did you make that happen? What do you say to them? Because you shared with me once that was a question you get asked a lot.
Yeah, it was a convergence of factors, I guess, so to speak. I think it was a mixture of it was good timing because I had been involved on the Exome Sequencing Consortium, which was eight different pharmaceutical companies funding that, and that was wrapping up. And we had a conversation amongst the eight of us of, what would we like to do next? Do we want to do something next? And we basically took a straw poll of other multiomics techniques and proteomics really rose to the top. So
I saw that as an opportunity. I was a huge fan of proteomics to make my pitch to that group of companies. And it seemed to go down well, but the timing just happened to be right because the field of proteomics was maturing to the point where these multiplex technologies could capture quite a sizable proportion of the canonical human plasma proteome. And it just happened to be a time where the pharmas had budgets set aside to do something innovative like this. But yeah, and had a good network of
people helping me out. Melissa Miller from Pfizer was a huge proponent of this alongside me, and Lyndon Mitnaul from Regeneron as well. So lots of different people, just basically all coming together and agreeing that this was a good idea. I have to just tell you that I was talking to someone on a different interview, and I said Melissa McCarthy, because Mark McCarthy and Melissa Miller were both involved in this. I just made that connection just now, as you said that. That's funny.
Now, timing wise, you mentioned that you started talking 2018 2019, if memory serves correctly. I think there was a press release at the end of 2020 announcing the UK Biobank's involvement. So that must have been a very busy year and a half. Yeah, I've always had these bags under my eyes, but they got bigger in 2020. The first proper conversation that we had about this was in Pfizer's New York campus in, I think, May. Sorry, February or March, I should say, of 2020. And I gave the pitch there.
And yeah, then everything shut down. The whole world shut down. So, the rest of the pitch was virtual. So originally we got six of those eight companies signed up, and then getting the other seven on board was all meeting people for the first time from different pharmas that it was all through Zoom or Microsoft Teams or what have you. Do you think that Zoom
was an impediment? Or do you think it actually because some things, oddly COVID, and this push to Zoom and teleconferencing kind of ushered in telehealth that probably brought us a decade forward in using telehealth solutions. I'm just curious about your perspective on whether you think that helped or hurt or was neutral. I have a silly perspective on this. I like it. I actually thought it was helpful
for two very silly reasons. I think the first is that I can be awkward in person, and I'm not very good at small talk. So Zoom is very you get online and then you get straight into it. I've seen you in action and you do get straight into it. You get things done, and then I'm short. I'm like, five [foot] seven [inches], so nobody can see that on Zoom. Those are two very valid reasons. Cut out the small talk. And I just took us down a rabbit hole, but I love it. You mentioned about multiomics.
How will you see the value of using multiomics in big cohorts like the UK Biobank? And what is the position of proteomics? How will you see Protonics position in this multiomics approach? Yeah, that's a good question. And sorry if this sounds a little
rehearsed. It isn't. But I've given so many talks on this at this point that I'll probably say the same thing that I often do, which is that we've been using UK Biobank and FinnGen and these big population biobanks to make links between gene variants and diseases, and then turn those links into new drugs. So gene "X" is a really strong association with disease "Y". Let's turn it into a new drug. Let's make a small molecule or an antibody that hits the protein that's encoded by that gene. Now
that hits the protein. We're not measuring the protein, and that's the issue. We're doing GWAS, we're finding lots of new genes, and a lot of them are intriguing, but a lot of them are very difficult to drug. And a lot of the time, the gene association that we've identified, it's messy. I mean, it takes a lot of downstream work to pinpoint exactly what gene it is. And oftentimes, it's either not completely clear or it's very pleiotropic, where it could be affecting a lot of different proteins
or pathways. So, really, I always thought proteins as the missing piece between genes and diseases in that genetics guided drug discovery process. The proteins, we could argue about it about how much they represent drug targets now that we have gene therapies and siRNAs, et cetera, that don't necessarily target proteins, but still, especially for bigger pharma, the vast majority of the drugs we're
making are targeting proteins. So let's put our drug targets part and parcel of that genetic drug discovery process, and then we have the potential to maybe reveal something mechanistic about how that drug is acting as well. Exactly. Yeah. And from the pharmaceutical drug discovery angle, they intuitively sort of picked this up, meaning they accepted that premise that we go from genetic guided drug discovery to gene, to protein, to disease. I hope that they liked
it. They seem to like it because they invested in the PPP [Pharma Proteomics] project. But, yeah, I think that it wasn't a difficult argument to make, because I think people have seen there've been a couple of papers from AstraZeneca and Abbvie and others, and they've looked retrospectively at their drug development pipelines. And they've basically assessed, okay, which drugs made it to patients and which drugs failed, and then which drugs had support from GWAS or ClinVar association
and which ones didn't. And there have been a couple of independent studies that have shown that if your drug target has supporting evidence from genetics, then it's at least twice as likely to actually succeed. But there's a lot of unanswered questions there that seems to be pretty good evidence. Yeah, okay, let's use genetics for drug discovery, but there's a lot of murky stuff in the middle that we still need to figure out. So I think that's where the multiomics can
help. And as far as the Pharma Proteomics Project being, frankly, you can say it's a pilot, right? Because you're looking at one-tength the size of the UK Biobank. You can also make the argument that, well, something like this has not been done at this scale before in terms of looking at 1500 proteins. Were you pointing to other work that had looked at circulating proteins in genetics as far as mendelian randomization, that kind of thing? Yeah, absolutely. There's been a couple of big studies.
Claudia Langenberg is one of the pioneers in this field. She's awesome. Well, I didn't prepare for this. I'm worried I'm going to leave people out. But there's Claudia, of course. There's Kári Stefánsson in Iceland with deCODE [Genetics]. Yeah, several different ... There's the SCALLOP consortium that we're doing this at a, I won't say smaller scale because they'd amassed quite a large collection of Olink data, but just based on the old panels. So kind of
90 proteins at a time. So there had been a lot, a lot of precedence. This definitely wasn't the first time anybody was doing this. It just happened to be the biggest so far. So their appetite was whetted. In terms of these smaller studies, they knew that this approach could work and therefore that was really a low risk decision. Do I understand that correctly? To a certain degree. I think that there were two ways you could
have pitched this. You could have pitched it to geneticists or you could have pitched it to biomarker experts or proteomics experts. And I felt that the pitch was easier to the geneticist because genetics for at least the last 15 years, if not longer, is used to doing things at very large scale. You need to do things in tens and now hundreds of thousands. And some of the GWASes are now even in over a million now in order to pick up the biology, in order to pick up the gene variants that are
influencing your disease. So they're used to doing things at really large scale. I think that they don't need to be convinced of that. I think the biomarker folks are more about let's do it with precision. I think that they still needed some convincing that we could do this at massive, massive scale. But do you think the NGS [next-generation sequencing] approach help you to make your pitch to the geneticists because it's an NGS approach and maybe they are more
familiar with this approach? How was your feeling? Yeah, I think so. I think a lot of folks had used Olink before, I think using the prior sort of method, the PCR-based method. I think that we'd seen some good quality based on those data and felt that the jump to NGS would allow us to scale up like this. What is the next step from the UK Biobank? What's your ambition actually first, and what's the next step of a UK Biobank? Yeah, obviously it would be great to do all
half a million. And I think that we're talking about that. We're having early conversations about whether that will be feasible financially more than sort of technically. I think that it's starting to become technically possible, but we have to talk about how much it would cost. I think in the shorter term, we're hoping, and I hope I don't jinx it by announcing it here, but we have received approval to do a smaller follow-up study in 2500
samples in the UK Biobank. And these 2500 samples have already been profiled using the Olink Explore assay. But we're going to do three mass spec-based platforms from Seer and Biognosys and Eliptica, as well as SomaLogic and then we'll just have a very comprehensive characterization of the plasma proteome in these 2500 people. And some of these people would have had COVID before
they entered the study and some didn't. So it's sort of like, let's try to capture as much of the plasma proteome pre- and post-COVID as we can. That'll be so interesting, I think, especially to see how the complementarity of these technologies wins out in a big cohort like this. What are you able to reveal if Seer has this vision to be able to sequence the proteome, try and look at things that aren't targeted, whereas some of the others, are - we go
after targeted proteins. And then I think these mass spec technologies are well established as gold standards and have advanced very far in the last few years in throughput. Because you're looking at different things, right? In terms of what kind of overlap there is with the canonical protein or versus sort of splice isoforms and all the variety of proteoforms. I mean, oh my gosh, there's what, 400 different types of post translational
modifications. I mean you can just start multiplying numbers together. When people ask you, because they've asked me this, Chris, how many proteins do you think are there, including proteoforms, what is your thought about that? You can't have a wrong answer because we don't know. It gets kind of mind boggling to think about, because obviously, without the proteoforms, you would expect there to be 20,000 just based on the human genome. But then it depends on how many
you can capture in blood. In terms of how they're expressed in different tissues, proteoforms, I don't know. Whatever I say will probably make me sound dumb. Especially in five or ten years when they work it out, like 100,000, maybe. Yeah, that's what I've said. I think I read somewhere someone made a good argument around that. Maybe it was Karsten [Suhre], maybe it was Jochen [Schwenk]. I don't know. Someone
said that. It reminds me of the speculation of how many genes were in the Genome Project. The numbers were all over the place. I mean, who would have guessed it would be a little bit less than 20,000? I mean, not that many, right? A lot of people really thought it was a much, much larger number. 100%. Yeah, exactly.
And then, as far as I understand that impending - or I'm sorry, already we've got released the data in terms of the Olink first 1500 [participants in the UK Biobank] against the 50,000? Yes. I think that they are on the Research Access portal. Now, don't quote me on that. I do not represent UK Biobank, but I think that they are. Naomi told me Monday, no told me Friday that she said it was up there. So by the time this podcast comes out, I think you're pretty safe.
There's several weeks-long lag time here, so we're looking at May 2023, the first sort of set of roughly how many samples? It's probably about 54, maybe 52 after QC. A thousand samples. 52,000 samples times some 1469 or so, give or take, proteins analyzed by Explore 1536. I mean, that's quite a data set for people to dig into. I think - go ahead.
No. Go ahead, Dale. Sorry. I was thinking about all the posters at ASHG [American Society of Human Genetics conference], right, in October that we were talking about on the podcast, as far as how many there was, what, 19 or so abstracts of different types of work. Yeah. This is not bragging. I have to keep track of this so I can convince people at Janssen and other companies that this is a return on investment. But yeah, you should brag. I think it was 19 abstracts and six talks at
ASHG. But what I'm really excited about the public release is that's obviously a lot of output, especially for a data set that's so new, but I don't even feel like that's not scratching the surface even. I think there's going to be so much more that
academics can do. There's a lot of creative things that you could do with this data set that might not have immediate translational impact for drug discovery, but academic scientists are going to take this and probably do something really revolutionary with it. I can't wait till next year's ASHG once these publications start getting into the literature, right? It's going to be all over
the place again. Is that because there's such a wide variety of different phenotypes that they can associate protein level and genetics to? Yes, well, yes, to a certain degree. I think we've looked at that. I think probably a lot of the companies have looked at that. We've done kind of an all by all. Take all of the ICD [International Classification of Diseases] codes or the feed codes, and then run a regression against all the proteins. And that basically gives you a crude biomarker
study. And we've been using those results in house. But I'm mainly thinking just about how there's so much creativity out there in the academic community. There's questions that you could address with these data that we probably haven't even thought of yet, because this was UK-PPP was like, one project out of several on our plates and pharma. So I think that fingers crossed, the academic community will have a lot of fun
ideas. Well, we had pushed out an Explore 1536 data set when we first launched that Explore platform, and it was on COVID. And there have been publications spurred from that by just comparing those COVID data in that cohort over to whatever work the researcher was already doing to look at those different signatures. So seeing publicly available data spur novel comparisons and novel publications. I just think that's what
it's all about, right? Getting these creative minds on it, crowdsourcing these ideas and how people debate ways to do things on Twitter, I just absolutely love. It's fantastic. On UKB, I think it kicked off in 2006, so it's not a new study, but it always feels new. They're always adding cool, innovative new technologies to this data set. So it'll go on for a long time to come, for sure. And then as far as you being how,
do I say that, that organizer. You were there at the beginning, you must have lots and lots of invitations to give these kinds of talks. As far as UK Biobank and the PPP in particular. Yeah, I do. Don't ask why did he accept ours? No, I do. Yeah, it's exciting, and I want to make sure that I spread it around. I guess I am the P.I. for the study overall, but there's no way this ever would have happened without a lot of other, more talented and more intelligent people than me involved.
So, I get invited a lot, but when I can to try to forward along to other folks who help build this
Melissa [Miller], Ben Sun, Joseph Stakowski, and by the way, Brad Gibson from Amgen was a huge proponent because the consortium was 90 something percent geneticists. Brad is the proteomics expert. Brad has the real background, the hardcore background mass spec. So he helped put guardrails on this and make sure that we were doing things properly. Make sure he got the ball over
the finish line, too, right? In terms of that extra weight of somebody who is not coming from the genetics field, but within the pharma proteomics sort of context. I think so. I have probably built a little bit of a reputation in this study, but I didn't really have any before I started it. And I think when I was pitching this idea, there was probably a lot of skepticism, like who's this little twerp? And he has his genetics background. So Brad being on board and putting his
weight behind it. Mark McCarthy, as you mentioned earlier, Cindy is involved as well. And Carrie, there were a lot of people that are more prestigious than me, put their weight behind it, and really helped put it over the finish line. Well, they got behind your vision. That's got to feel good. But how will you see - I have a question now, generally more for the cohorts. How will you see the use of cohorts in pharma and the drug development
process? I mean, what is the value and what do you think is having different, also ethnicities and different, let's say, from different places the cohorts will help on that? What is your vision? How's your idea about that?
I think that they're the engine for sort of epidemiological health studies, basically for any sort of common complex disease, we need these population cohorts to gain a better understanding of their molecular mechanisms, the causal mechanisms, as well as potentially some of the environmental influences on these diseases. So, I think that we've done a lot with UKB. There's a huge push now, a very well deserved push towards looking into underrepresented population cohorts.
So lots of different ones that we could potentially look into. And also disease enriched cohorts, cohorts that might have a dementia wing, for example. I know that Finngen is building up its dementia substudy, so lots of different directions that could go in. Is there a threshold, a minimum threshold, maybe because of incidence of disease in these cohorts or something? Like do you not tend to look at anything less than 10,000 samples or anything
less than 50,000? Or do you look at each cohort on its own merit, based upon is there longitudinal data? How are the data collected? Can you share a few criteria that you think are important for selecting cohorts? That's a good question, and actually, we've started to think about this more objectively. Can we put together a list of criteria for biobank
curation? Because now that the UK Biobank used to be the only game in town, but it's still probably, in my opinion, the best, but there are a lot of excellent cohorts coming out as well. The way that I think of it, and this is a little bit coarse and maybe crude, but the larger your sample size, the less detailed your phenotyping and your clinical information, and then the smaller the sample size, the more disease specific clinical phenotyping you
can get. So I would say you could go all the way up to some of these medical records databases from Optum or IBM, and they've got hundreds of millions of people. And you can do some cool things with regard to comorbidity mapping in those databases, but you can't link to a specific clinical scale for depression or Alzheimer's disease and they're not going to have neuroimaging or
proteomics, et cetera. And then on the other end of the spectrum, you have some of the cohorts that, like I mentioned at the start, the Swedish Neurology cohort that I was applying Olink to a few years ago that's got CDR [Clinical Dementia Rating] summary boxes and mini mental state examination and all of these very disease specific measurements that really help us drive in on specific hypotheses that are relevant to disease and sometimes almost use those studies like
natural history cohorts or like control wings to clinical trials. And then you have sort of the, I won't say the "Goldilocks" biobank, but the goldilocks sort of approach, but I can't think of a better term. And that would be where the Biobanks fit in, I think UKB it doesn't capture everything, it doesn't have many mental state examination or CDR summary boxes. But it does have fluid intelligence
tests, it has trail making. It has a lot of different cognitive and functional tasks, paired with deep genetic data. Now, proteomic data, actigraphy imaging, et cetera, et cetera. So, I think that finding that sort of goldilocks approach where we can get the power of large scale, but also get some of the denser clinical phenotyping, is usually how we try to go about it when we're selecting our cohorts. That's amazing. Wow. That was really
a rich answer. Thanks. Well, Chris, it was great having you here on the podcast this afternoon. Thank you, really, so much for being so generous with your time and your thoughts today. We really look forward to seeing some results. And indeed, like you mentioned, the creativity of scientists. I'm very happy to be here.
Before I go, I do want to give a shout out to Evan Mills again for helping get this over the line and to Klev Diamanti and Philippa Pettingell, who did so much technical and just all sorts of technical support and scientific support for the UKB project. A disclaimer: this was Chris's shout out to three Olink employees in recognition for all
their effort on this. But I'll also say that I think Klev and Philippa have both said how much they appreciated, how much they learned from the genetics perspective, from so many of these thought leaders that are the scientists in pharma who are driving the experimental design and the vision and gained approval to use the UK Biobank data. I think this idea of looking at pharma as a funding body, you shared that with me before,
Chris. These are heavy hitting scientists that have an incredible track record of being able to drive such rich discoveries. So it's such a privilege to be around you all and see this paper coming out from these data that you've all been a part of. So I look forward to that publication, too, in case you can plug for it. I don't know if you know any timing around the UK-PPP paper. First paper. I resubmitted the revised version over the weekend. Someone else was working over the weekend then.
The response to the reviewers was 29 pages long. That can either be a good thing or a bad thing A lot of novel methods, I think. Well, that's exciting. That's exciting. You heard it first here. Yes. Plenty to look forward to. Thanks again very much, Chris, it was great. Thank you. Yeah, good to be here. Thank you for listening to the Proteomics in Proximity podcast brought to you by Olink Proteomics. To contact the hosts or for further information, simply email info@olink.com.