Empowering breast cancer proteomics with genomics with Dr. Jochen Schwenk

activity. That's great. And I saw also that you study biochemistry in Tübingen, University of Tübingen. That's quite famous from the biochemistry industry worldwide. Do you have any story that you would like to record for your first paper? For example, being in Tübingen, and that would be great to hear. Yeah, Tübingen of course gave me a fantastic time. We were very small number of students per semester. I had very close connection to the

professors. I got a chance to go to a Lindau Nobel Laureate meeting there. And it was really, I think, an inspirational time that sort of created a lot of curiosity about science. And then after that I moved a little bit more into technology. So in the early two 2000s, when I did my Masters and PhD, I worked with Luminex-based assays, which at that time was really new. Then sort of that took me then to join the Protein Atlas in

2005 as a postdoc. And somehow I got stuck with this fantastic project. I'm still around and learn every day something new about proteins. I mean, just being there and to work with Mathias Uhlén and all the colleagues has been truly inspirational. So you've been at SciLife Labs since 2005, and that was when it began at KTH, is that correct? Yes. SciLife lab was

inaugurated in 2010. So actually, it was my and three other groups that moved into the building under construction, I think it was in October 2010. So I consider myself very much of an oldie when I think about my time at SciLife Lab. I've seen it change, grow, and now, I think, become a very prominent research institute in Europe. So it's, I think, fantastic and very much also gave me opportunities to learn about other technologies and to learn how information about

proteins can be useful. And I have many stories to tell, but one of which is, for instance, I have a little bit of a side activity project around GPCRs, and that, for instance, I think wouldn't have been possible if I would just be sitting somewhere in a lab and not be exposed to all these different activities. Sure. And for those not familiar, GPCRs or G- coupled protein receptors. GCPRs, is that correct? G-protein coupled

receptors. G-protein coupled receptors. I got to get my acronym straight. It's a really important drug target, right? Membrane proteins. Yeah, membrane proteins that are important drug targets. Correct? Exactly. Yeah. And they're at the SciLife Laboratories. Well, you said that you were involved in the Human Protein Atlas way back in 2005, so therefore the genome had just been finished in 2002. 2003.

Those must have been pretty exciting times because there was a big pivot and interest and focus on the proteome, is that correct? Yeah, of course. And then at that time, Mathias and us were producing all these antibodies was fairly unique, and people were not really sure whether that would add any value to the field

that's dominated by mass spectrometry. But I think now we've shown, and the way that we've brought in new data, trying to understand the data that we generate, and then sort of give feedback to other data types with localization of subcellular compartments of proteins that I think are really super valuable and help us to disentangle the complex biology that we live in. And my real interest is

proteins in the circulation. So that's even more complicated because you're under sort of the constant exchange of molecules in all parts of the body. So it's not as organized as looking at subcellular localizations, but it's still fascinating. And I guess that's something I really sort of fell in love with, and I really enjoy doing. Great. How would you see Olink because you are a biochemist? I mean, you are a mass spec expert. How will you see Olink fitting on this pipeline of

mass spectrometry? How do you see mass spec and Olink working together from your experience? Because you have a big experience in this field. I'm very fortunate to get to know Ulf Landegren many, many years back, and we've been sort of seeing each other on a regular basis, partly because Uppsala [Olink] and Sweden [SciLife Labs] are very close and sort of been

doing things in parallel. And of course, fantastic to see the journey that with the proximity extension, proximity ligation, and all these different versions of this concept have now, I think, been the driver for using antibodies as molecular tools. I mean, there was just a paper in Nature Methods, I think, just the other day. Again antibodies conjugated with oligonucleotides. I think that's giving people much more bigger field to play and use these

reagents. So of course I've seen how it started and we've been very late to the game. My lab, or the unit that I'm heading at SciLife Lab, started to introduce Olink in 2017. And since then, we've been super happy to have the system in-house and do this for others, users that come to SciLife Lab to just want to have data, but also for our research. So I think, again, any data type adds a value to what we do. And I think Olink has truly enabled us to do many things we weren't able to before. So,

fantastic. I'm dying to ask in front of you, based on what you see, the opportunity in front of you with this technology, whatever technologies, right. What is it that you're most excited about for the future? Are there aspects of work that you've been doing or a direction that you're going in, that you would like to share, that you're comfortable sharing: I just want to know what's the part that makes you go into flow? What do you want to do next?

Yeah, I started doing a lot of assay development myself when I worked with Luminix 20 years back. That sounds a little bit silly when I say this, but it's the truth. Of course, that sort of has always been something to try, something new to maybe try, something that's difficult, maybe not immediately rewarding, but in the long term, something that could be very fruitful or something that makes you proud as the researchers that you say [to yourself]: "Okay, this is something I believed in,

and I see it's happening." So the next sort of moment for me, when I had this type of thinking was when COVID started and when lots of people went into serology testing or protein testing in the classical way, when me and my colleagues at KTH, we said, "Let's try something different and use dried

blood spots. Let's not ask people to come to the clinic, let's send the devices back to them, to their home, so they can collect bloods in their kitchen and sofa, wherever, and send them the samples back to us, to the lab, where we can do the research." So that, I think, really inspired a lot of new ways of doing this. When you think about cutting costs, simplifying workflows, freeing the time of people in the clinic, but also to think about

doing health monitoring. I think people always often ask me, what do I think is proteomics best used for? And I think it's monitoring. It's like looking at who you are and looking how you change. And I think this combination, I think, now is sort of shaping towards something I really am passionate about. And dried blood spots is a fantastic tool. It's more challenging than doing it the classical way. There's so much more to learn, and then maybe even go

further. When Sarantis and I talked a couple of weeks back, also to look at even smaller sample volumes, looking at other body fluids, such as interstitial fluid, that could even tell us something extra that blood is not able to tell us. We'd need a baseline to understand the reference of dried blood spots to be making that comparison, right? Yeah. And so enabling in areas where we can't get a blood draw, a phlebotomist out to do a blood draw. I think that's going to be really important. Dale?

So if you can give us some background on dried blood spots, and I'd appreciate it, because my only familiarity with it was when I had my first child, and they did a heel prick, and then they went ahead and used the blood from that little lancet onto a particular card. Is there something special about the material they use for a dried blood spot? And what are the challenges as far as working with proteins in that context?

I think let's say if you think from an analytical perspective and a precision perspective, the dry blood spots your kids donate, they are just put in a filter paper to do a plus/minus test. So it's really a binary answer you're after. But if you really want to look at subtle changes in the human phenotype, I think then you need to ensure that the precision of the material you use in your system is there.

Normally when you have a dry blood spot, you get sort of a donut distribution of the red blood cells, so it really matters where you do the punch. So you want to avoid these type of things, especially if you want to do it at scale, if you want to do it consecutively. So I started to work with a local company that was founded by one of my colleagues at KTH. And they use a microfuidic system to exactly collect

ten microliters. So just knowing that what you put into your system is ten microliters, of course, then there are different levels of hematocrits, there are different other things that you need to consider, but you at least eliminate some of the concerns that you have. That, I think, is really the key. And of course, it's a simplicity of this procedure that you can assure it's easy for people to do. And they manage, even though they may not

be trained. I failed also when I did it the first times. But if you get used to it, the quality is really excellent. And I think there are also studies showing that more and more are using other devices that are out there, I think now you have a new material which is sort of similar to plasma, but it has some bonus. And the question is, how do you manage that bonus? Is it something that is a challenge, it's a burden that makes it difficult for you to be analytically

precise? Or does it open up opportunities that were not possible when you looked at the regular blood, plasma samples? Because of, let's say, the hematopoietic cells that are still there, they may leak out something that could be really exciting. So it's this balance between things that, I think ... When you have a discussion a little bit with other proteomics expert about dry blood spots, there's always a question about how you control and

normalize. Because, I don't know, from what I have heard, actually, it's not easy to have always the same type of dry blood spots. I'm guessing that there's a lot of varieties, a lot of variation can be there. Would you have any idea how one can normalize this data in order to have like, longitudinal studies or studies for different cohorts? Do you have any idea on that? That would be great to hear, actually. Yeah. I mean, there are analytical concepts that you can think about to do

precision. You, probably similar to other studies, try to find some housekeeping markers. And we found some, for instance, that are related to skin. The skin, when you do the landset, or punching through your upper layers of the skin, these proteins will probably always be there. So trying to figure out what are the markers that are constant. And then again, what we talked about before, if you have a phenotype that is changing, then you can sort of do this

resampling. You can learn from the resampling what are the constant constituents and what are those that are variable, what are those that are unreliable. And again, the more data you have, the easier it is to make that exercise, because you can rank things in a much more refined way. That's very knowledgeable. You mean using housekeeping kind of housekeeping proteins in a way, right, to normalize? That's pretty much the idea around? Yeah, exactly.

And then, of course, it's just also a matter of using different statistical models to do normalization and things like this. I mean, whenever you have a variable sample source, I guess we have that also in plasma, different degrees of hemolysis, different fat content, different hydration states, they can influence so many things. So I think just keeping being on your toes when you look at the data and not get carried away too quickly, I think something that's very helpful.

Coming back again, and I'm sorry I monopolized the questions, coming back again to Cindy's

informative. So again, this study we did on interstitial fluid, we also detected, for instance, antibodies against SARS-CoV-2 in interstitial fluid. And then of course, that is a proof-of-concept we did because we were curious and we had material, or we had plus/minus as the phenotype. But imagine you're treating someone with melanoma, with a biologics. How can you assure that the biologic actually reaches the area where it should act?

I think these things could, of course, be much more informative than looking at a blood sample where you say, yeah, it's in your system, but we don't know if it actually reached the point where it should be doing the job. So again, these things, I think, open up new ways and, and trying these these new methods of sample collection. And then, of course, having the perfect tool that analyzes these

samples. And again, the fantastic low volume requirement of Olink has, for us, been this perfect match. So we're super happy that we have a tool that we can test these ideas and we can demonstrate it's actually feasible. To return to what you're excited about in terms of these longitudinal studies, have you had much interaction with the UK Biobank in terms of samples at scale? I guess you don't have to worry about sort of the dried blood spot collection. I mean, that's really promising, but

here it is. We have a huge data set. Have you been involved much with the UK Biobank? Indirectly, yes. I mean, I've been talking to Chris Whelan and others. And of course, when Karsten Suhre, Mark McCarthy, and I started to write this review in Nature Genetics a couple of years back, we thought of UK Biobank as the audience, especially Mark McCarthy who I consider my mentor. He was in Stockholm and I talked to him and said, "Mark, you're doing this fantastic

work. And I think proteomics like Karsten Suhre has shown, is a perfect match with genetics. Can't we write up something as bringing different perspectives together into one piece of information?" And that's sort of how this whole idea started. We actually called up Karsten and said, "Karsten, we have this idea, do you want to join?" And this is sort of where we joined forces. I learned so much about genetics, and others learned about

proteomics. So I think that sort of was, of course, the dream coming true as writing something that adds value, but learning something at the same time. And then, of course, UK Biobank being, as has been shown, a fantastic study now being powered by all these new data that is coming out. But, yeah, again, it's often a one timepoint picture, but we want to create a movie of our lives, right? And the movie tells the

story much better. And we should probably just explain Mark McCarthy, although I don't think he needs an introduction. He's such a well-known figure in our world, certainly, but he's at Genentech, of course, but he's one of these geneticists that has crossed over into industry. And just anything he focuses on I like to keep an eye on, because it moves and shakes. He was at the International Congress of Human Genetics, and so involved in the leadership, talking about how to

increase diversity in genetics. And I love that Nature Genetics paper. So I just wanted to say, "Karsten, you, and Mark M, it's just such a pleasure to have you even talking about our technology. It's very exciting." And I think, of course, we wanted to be as agnostic and fair as possible, because I think every technology has its pros and cons, and I think it's up to everyone to make a decision what is the best fit for the situation.

Absolutely. But I guess coming back to your question about longitudinal studies, which we've been also doing locally, led by Mathias Uhlén, and we've been working with Jochen Schwenk from the SCALLOP cohort. You know, of course, that that is when it all sort of comes to life, right? When you see a signature, you can understand stability, you can understand that a person has had an infection, things go up, things go down, but someone

loses weight, things change. So that's when the information actually becomes clearer. And that's a

or 2019. At any rate, the ability to monitor real time health as people transition from a state of health to one of disease.

I finished a book recently, "The Age of Scientific Wellness," from Leroy Hood and Nathan Price, and it talked about these disease transitions, where if somebody's healthy, they don't have symptoms, but something's happening in the body, something's happening with their metabolism, something's happening with their metagenomics, something's happening with their proteomics and the circulation. And that is just this fascinating thing because you're talking about wellness, right? We need to be

sampling "well" people. And I think the UK Biobank gives this unique perspective. I'd like to hear your perspective on that. Yeah, of course, I mean, UK Biobank offers, as far as I understand, really a range of phenotypes. I assume some involvement was in selecting particular sort of disease groups and enriching them for those that are maybe more prevalent than others. But just to have that breadth is really amazing because often you're limited to certain sample collections.

And maybe I take another sort of, open another bracket and take a little detour here. But again, when you do this dried blood spot random sampling that we did, you include everyone. You don't include only the ones that are sick, and they only come when they're sick. So, you know, okay, CRP [C-reactive protein] and all the other friends, they're all already up, right? But we want - so how do you get that cross sectional, that true sort of population-based

variance? And I think that's only possible in a coordinated way, like UK Biobank did. And there are other biobanks that all of us in the U.S. and others are trying to do similar things. That when you learn this is the human variability with all the genetics, with lifestyle, with social economic factors influencing who you are on a molecular level. So yeah, fantastic. And then having proteomics in that play is of course something I get particularly excited about.

And it's this combination - I'm sorry, go ahead. I'm sorry, Dale, go first. Oh no, Sarantis, this is your show. Go right ahead, please. Thank you. Now I'm talking about how you mentioned about proteomics, genomics and disease, and there's a preprint with Anders Malarstig now that recently came out, and they're going to be soon published - finger crossed - with Olink. Would you like to share a little bit information about the research there and the cohort that you use and what's your

main findings? Because it's exciting to use the circulating proteome to identify prognosis [biomarkers] for breast cancer, early prognosis for breast cancer. I'm happy to hear a little bit more from you, actually. Yeah, so we're talking about the KARMA Cohort, which is a Swedish breast cancer population cohort that invites all women in Sweden undergoing mammography screening to participate. This is spearheaded by Per Hall and Kamila Czene at Karolinska

Institute. And with both I've been collaborating already for quite some time and we recently got some funding to continue our collaborations and then brought in also Olink data that we generated in the lab to look at breast cancer risk. So in addition to this paper that you mentioned, Sarantis, there's also another one that's been circling around now where we wanted to primarily identify - Can we use proteins to predict short term risk of

breast cancer? Genetics can do that on a more longer period of time, but can proteins add something to it? And then, of course, with Anderson and the leader and the SCALLOP consortium, and we want to also to bring also genetics into this. And I'm not a geneticist. So for me, again, it's always fascinating to see proteomics data in action.

Which is what it makes me most proud because I think what it is exciting to learn when other datas inform you about your own data and when others take the data that you generate or that you know more about and they tell you new

stories. And then, of course, the KARMA cohort is really a population-based code and it's really unique in a sense that we're not only looking at breast cancer cases, or the study could continuously collect sample patient information or personal information, and eventually some of these persons will become patients. And luckily, then we would have, let's say, a blood sample from the last time when the patient was still a person, so to speak, when you

think about these two categories. So we can go back in time and see are there any things in the prior history that could lead towards okay, you are actually on a much different trajectory than the remaining individuals, so that's all that people have different lives. We know drugs played a role, pre- post-menopausal plays a role, hormonal replacement therapy plays a role.

So lots of things happen. But then genetics can tell you an unbiased story about all these phenotypes and that, again, gives a new angle to this whole problem. And in this study, we used Mendelian Randomization and found five interesting proteins that presumably have a causal role in breast cancer. And of course, this is the study. Now, it's only nowadays 600 individuals, but still, it's a really very fine selection of samples that could

lead the way. And then again, taking the road that genetics has taken, we can use data that exists in other biobanks and we can sort of look, do we see the same associations in these? And that's, of course, when multiomics doesn't become a picture, it becomes a movie, where we take different [aspects] of these relationships. What a great illustration and analogy. That's a great analogy, right? Not a picture. We've got a movie. Yeah. And I think that's what we need, right?

You take a look at a picture and you interpret so many things into this, whether you know something about the painter or the time when the painting was made. But if you have a movie, it tells you much more. It tells you a dynamic that you cannot really see in a

picture. But anyway, so again, we had this opportunity, and then, Asa [Hedman] and Anders [Malarstig] have been really leading this together with colleagues at Olink and others, to sort of find out whether these things we identified in the Swedish KARMA study also we can see in the UK Biobank or in Finngen and it seems so to be the case. And of course, that gives much more certainty about that. These are interesting

findings to follow up. And again, I think what we talked, I guess before this podcast started, that then you can start to develop drugs, and you can see what actually happens when you give someone a drug that addresses one of these proteins. Then you, again, start a new movie, right? But, on a different direction. And again, then use proteomics to follow and see what happens. So, that's fascinating, I think. And so who does that follow up?

Are you involved in that kind of obviously, proteomics is your field, and I just wonder if there's another function that takes that to the clinical trial or to the test bed to try out these drugs that affect these pathways. Yeah. Of course, I guess it would require that we have the right partners who would have the libraries to do drug screening on these, and sort of it's an army of new things to engage.

But of course, primarily to see that what we do in these studies has a value and then again, translate it back to functional studies, which, again, is something I think will also happen in the next couple of years, is taking all these big biobank screenings back into some sort of functional studies to see, okay, is it really the molecule? Is it really the phenotype? Is it really the drug or the

lifestyle effect? And that's going to be sort of looping back where it started, from cellular studies into systemic studies, and then back into the - Wouldn't it be amazing to find a lifestyle effect that we never thought might have an impact? Yeah. Having the tools to be able to start parsing these things. It's fascinating. To talk a little bit about the

study itself, right? This Nature Preprint looked at 300 individuals from this mammography study who had breast cancer diagnosed over those two years, that they took a look at them, and then you matched it with 300 normal individuals from that same study. You had genotype information, right? And the genotypes combined with the Explore 3000 [platform] in terms of the 2900

proteins. So you had 600 individuals, 2900 proteins, and then you discovered 800 pQTLs [protein Quantitative Trait Loci] and controlling 737 proteins. And I thought that was fascinating, right? That we have genetic control of 737 proteins that can identify the variants pQTLs, and then you can drill down and get five likely causative proteins of breast cancer. Do I

understand that correctly? That these five proteins you identified were previously not investigated or investigated with certain sort of weak associations of breast cancer? But the take home message is that these five proteins were new discoveries. Yeah, that's our understanding. I mean, of course, maybe someone else has already figured this out, but not told the public about it. But I think an important aspect is also to say that these 300 cases, they were not cases at the time of

sampling, they were future cases. So when they were actually sampled, they were still considered persons, not patients. So that's, again, also to understand, then we have this list of proteins that all tell different stories. And I think it's fascinating to be sort of in your mind, thinking about what actually the role is. But what we need now is, of course, the hard data that tells us this is true, or this is actually the opposite.

And I guess to back up to the original KARMA study, which was out of KTH. There were some 70,000 women from Karolinska who volunteered. From Karolinska. Okay. 70,000 women, though, over a couple of years. Is that correct? I think about the effort involved. Yeah. This is the nice thing about doing science. In Sweden - I originally come from Germany - it's a different system. But in Sweden, maybe because of the Nobel Prize, maybe because of the public interest in science, there's much easier

engagement. And in women, I think, in other countries, have this regular sort of health checkup. So there's this mammography screening program, and then you get basically asked, "Do you want to participate?" And then Per Hall and his colleagues do the magic and keep people engaged, and people follow, which

is super. And then you mentioned briefly the power of replicating these results, because that, I think, is an important dimension of this paper, in that it wasn't just a single finding in a particular population that you were able to find. We're actually able then to go back to was it? Finngen and the UK biobank? And then look at the genotypes, look at the protein levels, and then being able to actually show, yes, this connection holds up. I think that's pretty significant.

Could you comment on that? Again, this work was spearheaded by Asa Hedman, but again, what I see is you have, let's say you create this currency, let's say the pQTLs. This is a currency you can go and you can pay in other countries or in other biobanks. You can use that currency to exchange information. And this is, I guess, what genetics has really enabled us to do. And now proteomics is learning how it can do it. We have different technologies, they may have different

outcomes, different information. But again, you can anchor it on the genetics. You can use the pQTLS, you can use them as instruments in mendelian randomization to exchange this information. And that's amazing. Yeah. Here it is. You're talking about empowering proteomics with genomics, right? Turning it around instead of coming at, I mean, I come from a genomics background. So I think of it in terms of proteomics adding

to the genomics. Here it is. You come from the proteomics background, and it's the genetics that is really enriching the findings. And I think that's great. Jochen, I have a basic question, and it's very basic. We mentioned that one of the factors could be lifestyle, like environment, but also could be the hormones. Do you have, let's say, relatives, like mother, sister, or twins

that you can control in this cohort? I imagine there will be also some twins that you can, let's say, somehow discriminate and identify the genetic background versus the environment. This is the first part of my question. The second part is for sure, you would check, like, post- and pre- menopausal. Then have you seen differences? What's your feedback on that? What's your experience around this type of observations?

Yeah, I mean, we have had previous studies that we published using other own technologies that we used 510 years ago, where we specifically looked at hormonal replacement therapy as one of the factors which, to our surprise, had really a long lasting effect on the women's proteome, which again, is really quite significant. And then, of course, that pre- and post-menopausal breast cancer. Again, this is something I've been learning from my colleagues that I work with, is very different.

Then, of course, you need to disentangle. So, I'm sorry, I want to click back to what you just said about hormone replacement therapy having a lasting effect on the proteome. What do you mean by that? Meaning that it shifts the proteome? But what about the risk, the cancer risk, right. Because certainly the women's health study here in the U.S. had led to some concerns around that. I'm just curious if that's part of the impact on the proteome,

do you think? Or maybe what we found in this other study is that we had a subset of women that really we could sort of see that previous use of hormones had a significant change in their proteome and also increased their future risk that they were developing breast cancer. So, of course, this really sort of showed up. But it's a small subset of all the women that we tested. It's a great thing to follow up. Of course, we still need to understand is what is the effect of taking hormones?

Do you actually have remodeling of some reproductive pathways that constantly do something? And if they get sort of, let's say, pushed off track, they will stay on that off track path for a longer period of time. And then there will be feedback loops with, let's say, the liver and other organs to just try to adapt with the sort of external trigger. So that was sort of part of our sort of understanding of

the use of drugs. But again, it showed that taking medication has a quite substantial effect on your proteome. And we found it fascinating that it actually seemed to be consistent over many years, a picture of real time biology, the proteome, right. And, what we know about effects of certain treatments, what we know about effects of certain drugs, we're just scratching the

surface. Right. A number of our pharma partners and customers of Olink are finding out so much with just a limited set of proteins. They're not looking at the proteome, they might be looking at a panel of 50 or 90 [proteins], or what have you. But there is just so much to learn about the biology. Sarantis, you started to ask, I think you were down a path of a couple of questions. I was curious.

I want to ask just a more philosophical question about if you have some, let's say, mother, sisters, some relatives, or if you have some twins that you can follow. And you can see the change comes from genetic background, or comes from the proteomics background, or combination, or neither. Do you have any experience on that? Have you seen some patterns around [that]? I don't think we necessarily looked into this.

But I've been working with another twin cohort from Sweden called Twin Gene, which the name says has a quite clear focus on these aspects. And no, not that I think in particular, but of course, it's, again, what we pass on to our children is something that will be, in the future, helpful for them to know. And maybe they will change their lives when they know. Okay. I'm at a higher risk of a certain disease because both my parents

passed away. I guess you see a lot of these breast cancer studies and effects in Iceland, I think, right. But not in these studies. I cannot recall that we actually specifically looked

into this. Yeah. What was I think really interesting about this particular paper on breast cancer is that you looked into so many different kinds of connections in terms of inherited risk as well because the title is paper, "Evaluation of Circulating Plasma Proteins in Breast Cancer and Mendelian Randomization Analysis," you're actually looking at, then, the entire genetic backgrounds of unrelated individuals and just saying what is elevating that particular

risk. And understand, these five proteins that were differentially regulated were basically lifetime exposures. That a person was exposed to a high level of protein throughout their whole life. And I think that's what makes this really fascinating, right? The proteomics being informed by the genetics controlling the levels of protein, and then saying these five proteins actually become drug targets, which I thought was just a fascinating realm.

Before we wrap up, Jochen, would you like to make any final comments? Either, I don't know, about where we are, where we're going, working with Olink? Oh, I understand, right? We didn't even talk about a very famous postdoc, famous at Olink, Philippa [Pettingill] came out of your lab. I don't know if you want to talk about what it was like working with her. She has helped me, Cindy, with her title. She's Director [of Application Sciences]. She's a superstar.

She runs the field application scientist team within the European region, and she is absolutely magnificent. She's also helped lead our discussions around statistical analyses in the UK Biobank Project. She's just such a magical human being to have at Olink. We're so lucky to have her. And she is a product launched out of your lab. At some point, you had an impact on her trajectory. So, yes, please, anything you have to say about her

would be greatly appreciated. We had hoped we'd be able to have her on, but we weren't able to get her into the timing that we had going. No, I mean, all the success that she has now is because of her engagement, her knowledge, and her curiosity. But, yeah, it was fantastic to work with her. She was with me about one and a half, two years. It was inspirational and fun from the first to the last day. And I think to see someone leaving the lab and making such a wonderful career

is fantastic. I guess if my contribution is that I showed her all these different tools that we had in lab, including Olink and others, and we talked a lot about the different assays, the different concepts. So if that has helped her in achieving these fantastic things that she's doing with you, it makes me proud and happy. I think she deserves it. And I wish, of course, her all the success, and anytime we see her, we see each other on the media calls, it's like old friends.

I think she came out absolutely a leader, and I think she has such great things to say about the time that she spent in your lab. And I think that's pretty sweet. Thank you. That's great to hear. All right, well, thank you very much for joining us today, Jochen. We've really enjoyed the conversation. Thank you for having me. It was fantastic. And continue with this great podcast. It's really a treasure. Thanks a lot for setting this up and running it. You're so

kind. Thank you. Okay, well, I think that's it. Thank you. 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.