Welcome to the Proteomics in Proximity podcast,
where your co-host Cindy Lawley and Sarantis Chlamydas 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 host, Cindy and Sarantis.
Welcome, everybody.
I'm back from holidays and it's my first episode for 2025.
Happy to see you all again.
Happy to see Cindy.
And I'm really excited to discuss with Jenny and discuss about proteins and,
yeah, looking forward to hear from you, Jenny.
Excellent.
So it's Cindy here, also here with Sarantis and our vice
president of product management, Jenny Samskog.
Jenny, there's a little bit of a question about how to pronounce
your first name, so I'd love it if first you told us about that.
Secondly, if you could tell us about, your role,
what you've seen evolve in proteomics, you've got a pretty prestigious title.
And today we want to talk a little bit about what's coming up.
In the future, we have a this recent launch that we'd love you to characterize.
And then we'll talk a little bit
about some of the meetings where we'll be attending.
Please take it away.
Thank you so much for having me.
I'm really excited to be in this podcast with you guys.
So my first name is Jenny.
So it's a soft J, that's Swedish.
And I
would just like to comment a bit on, you know,
where I come from.
So you understand my history
and I would say my main common denominator
is really protein science and product development.
So I did start my career in mass spec proteomics as a researcher.
And after that I refocused to support,
by biopharmaceutical research and manufacturing.
And my main function has so far been within product management,
which in essence means developing new products and ensuring
that the existing products that we have are meeting our customer expectations.
And you're so good at leading a team that listens to customers.
So I just want to acknowledge and appreciate you for that.
It's such a pleasure to be here and have a have a product management
function that really, really listens.
And I would say, thank you, Cindy, but I would really say,
you know, I joined Olink, what can it be like three years ago or something.
And their focus on innovation
and advancing proteomics is very special and very unique.
It was
for me, it was a match made in heaven because I could combine having
great products out in the market
that contributes to cutting edge science.
But the culture of innovation at Olink
has been there since start and no credit to me there.
But it's really nice to be able to continue that culture of innovation.
You picked us and we picked you.
It's a match made in heaven.
I absolutely love that.
What's your why? Why proteomics?
Why do you see such promise in this space?
Well, you know, I think it's been,
I just have to go back to where I started.
So I did my research a long time ago,
within proteomics and, as CMS or mass spec.
And at the time, it was a fascinating area, but it was early days.
So at that time, you know, identifying,
you could identify a handful of proteins.
And then I was happy, if I could say like 30 proteins or something
out from the mass spec.
And not only that, but, you know,
the way we identified the proteins could be based on one peptide.
And that peptide.
I had sequenced myself in the mass spectrum.
So there were very limited amount of digital tools to support that.
Not much like intelligence software or anything like that.
So that's where I sort of started.
That's where I,
and then I left that for it for quite some years, actually, to go
to this more biopharmaceutical, industry and then came back to Olink.
And it was, you know, it's
groundbreaking how much things have happened since then.
So, the fact that you can study thousands of proteins,
the connection that we automatically almost have to genomics.
It's definitely a new era.
So, I would say it's
a huge
thing that kind of happened in proteomics since beginning of 2000 until now.
Yeah.
I'd, I'd say that, and as you know, I've got a history in the genomic space
that we've been trying to get at proteomics from the genomics side as well.
From our first RNA seq experiment. Right.
So those first sequencers that Illumina made, the 1G,
they were going out the door for folks that were doing digital gene expression
at the time, had been using gene arrays, gene expression arrays
and were keen to to understand the links to real time biology.
And now, as part of Thermo Fisher Scientific, we have both the mass
spec size and some amazing innovations in the astrol and the stellar there,
as well as as this, proximity extension assay component.
But what do you
think it was there actually, that breaking point
that makes a difference for proteomics to be more democratic?
Is it the NGS itself?
Or the NGS plus other protocols that would be integrated.
What is your feeling there?
Well, you know, if we're talking as well about mass spec here now,
I mean it's not our a core area, but it's definitely our college area.
And you know, within mass within proteomics, mass spec is still a
gold standard but also here and remember, this is not my area of expertise anymore.
But there's a huge amount of things that have happened here.
And I would say mainly or a lot of things,
of course, on the technology side to make sure that we actually can,
have a much greater proteome coverage, obviously.
But also, the digital tools, I should say,
how do you understand the results,
how did you quality assess the results, etc.?
And the supporting tools to do that.
I think that's been for me.
When you've been away for a few years and you come back to see that
both in within Olink, obviously who is really spearheading this market.
But also, what I have seen from
the outside has happened in the mass spec area as well.
Yeah.
So just specifically around that Olink,
component within the Thermo Fisher
environment, you know, the proximity extension assay,
first launched on the qPCR readout,
in 2020, launched on the NGS readout.
So that transition to be able to look at more proteins across the proteome,
particularly in plasma CSF, some of these liquid media were
mass spec may not be able to see those low abundant proteins.
I think that that was game changing for me,
and that attracted me to this team and this technology.
Jenny, you just had an announcement from your team about this new reveal products.
Can you tell us a little bit about where that fits in to the democratization?
Yes. And so yeah, and thank you for
for highlighting the democratization because that's one thing.
So proteomics has been you know, it's
not for everyone or hasn't been for everyone yet.
One of our overarching goals,
within product development is to make sure
or to enable our customers to utilize proteomics,
and make it accessible to a broader research community.
And some of these accessibility
challenges that we're trying to address,
have been noted elsewhere many, many times.
One of them being cost.
So, to be able to run large proteomics studies or to be able to run
proteomics clinically, the cost needs to go down.
There is also, apart from that, a perceived complexity
within proteomics, right or wrong.
And there's also, a need to better understand the data
and to get more support in understanding and trusting the data.
So those are the three things that, you know, at least we can talk about today.
And and
before I go into our new product, I just want to mention
and we haven't really talked about that, you know, we talked about,
what has happened within proteomics,
in the last years.
But I really want to mention something that really made a big difference.
Is of course, the,
you can be first project,
where the data has been public now for over a year.
And we have so many,
new publications coming out from that project already.
So, and already now it's, you know, and that is sort of a game changer
within proteomics I really just want to highlight that.
Well, and those publications are highlighting which diseases folks
can dig into.
So just for context, the UK Biobank Pharma Proteomics project
a few years ago, 13 pharma partners agreed to run Olink as the technology
of choice against almost 60,000 samples in the UK Biobank.
Now, there has been
publications around,
the 54,000 samples
that have been part of the flagship paper that came out in nature
in October of 2023, and then there have been 200 publications
that I know about over 200, but that's been cited.
That flagship paper has been cited over 300 times.
And so that certainly builds,
more of a comfort with the actual data themselves.
It doesn't allay people's fear.
And I'll say geneticists fear, but just because I talked to a geneticist
around what we call pre analytical variation, and
I think you allude to this in your complexity comment.
Jenny, you mentioned right or wrong,
they're perceived as complex and I certainly think that's true.
From the genetics point of view.
And I know Sarantis has a history in this as well.
Actually that was also my question, I'm guessing that the daily life
is not only happiness in product management.
You have a lot of challenges to go through.
And you mentioned the cost.
You mentioned, the time that you spent on developing.
But any other challenge, especially from the technical variation,
that you may be facing?
That'll be great to hear.
Yeah.
So I think that that is really one important aspect,
especially as a supplier, to really make sure that we can guide,
our customers in understanding their data.
So maybe we can go through that a little bit because.
So when I talk about trusting the data,
that's
very critical but very often overlooked.
And proteins are different from genes.
They are a little bit more sensitive.
You have to really take care when you do the sample collection
and how you handle the samples,
and really ensure that, you know, you have you can assess the data quality
at each stage to build that confidence.
So one of the things that we're doing is to develop tools to help our customers,
help our researchers understand if they have pre analytical variation
and then guide them through, what that could mean.
Are they going to discard the data.
Can they use them anyway.
So it's sort of a like an understanding, like an intelligent support
of understanding your own samples, your own results.
And so it's really critical,
within proteomics
to really take that into consideration.
It's a great point.
And I think at the end, our main goal is the precision medicine right at the end
is like, having high quality data where we can enable precision medicine.
I'm sure Cindy, you have a lot to share, about this field
where you are really looking closely recently on that even more,
you know, and happy to hear your thoughts about
how do you see this precision medicine being enabled by proteomics?
And where do you see these going, proteomics in this respect.
Yeah, absolutely.
So I think we're better characterizing disease risk
in individuals because we're capturing real time information.
And so the comparisons of polygenic risk score to protein risk scores
have been really helpful in that regard.
There's some papers out of Claudia Langenberg's lab, as well as
Ben Sun, who's one of the one of the joint steering
committee members in the UK Biobank Pharma Proteomics project.
He's published with the team
at BioXcellerate and Optima Partners around protein risk scores.
I think that's going to help us in understanding
how to better recruit for clinical trials so that we can have clinical trials
that are smaller but powerful sufficiently powerful
to see success in candidates.
And of course the ability to have more successful clinical trials.
What do we say the candidates,
you know, 90% of candidates fail when they hit clinical trials.
If we can improve clinical trials by just 10%, we'll be the best
drug makers in history.
And then I would say that changes everything downstream,
because now we're really dialing in the right treatment for the right
patient at the right time, which Chris Whelan talks quite a bit about.
And he's in our just recent episode of the podcast talking about just that
and how proteomics is enabling this and so ultimately those are the pillars
I see being moved, the pillars of ultimately precision medicine,
which interact with clinical trials and risk stratification.
And then each of those interact with each other.
And I see all of those moving
upon the foundation
of an understanding of how genetics, proteomics and outcome data
are associated and linked.
Thanks.
Yeah, thanks for asking that.
And, you know,
there was there's been a lot of discussion
from our customers and the research community regarding
how can we, understand different technologies in this area.
How can we understand how they complement each other
and can you help us?
Sort of guide us
how we trust the data or how we analyze the data.
So I think that's going to be and that's normal
because proteomics is maturing in itself.
So I think
that would lead us back to a little bit on the mass
spec side where the complementarity between, for example, our technology
in combination with mass spectrometry,
could help us to better proteome coverage.
It could help us assess platforms
through mass spec, while we would maybe take more of the plasma side.
So I think those kind of things, and then again,
as suppliers and enablers to the research community here,
I think we have a role to play
to make sure that we really showcase that
these are, you know, what we show you, what you see with our technology is
you can trust that and you can and we will also guide you
in terms of understanding how that performs
versus other technologies and how they complement each other.
And I think that's going to be something that, as we are maturing,
we're going
to see more of and that's going to be a lot of them.
And well, investments in digital tools.
For that integration and for, for AI, machine learning, we hear a lot.
Mike.
And also, I wanted to ask you Cindy, for sure you have the overall
this trend of suddenly a lot of people, due to the fact
that we have a lot of technologies and other technologies.
Now we're talking about precision medicine, right?
And there are a lot of events happening around this, especially in ways
that they didn't used to have before.
What is your feeling and what is your feedback on that?
Because you are more in the field and,
you know, in discussion with a lot of people.
How do you see this moving forward?
So I think these two topics are very intimately linked.
You know, your reference to our activities in the field
and working with customers and showing up at conferences
and our messaging Sarantis, and Jenny, your comments about having
a responsibility in funneling data
that are as accurate as possible into,
the algorithms for machine learning and artificial intelligence
that will change our understanding of these large data sets, right?
We're not data rich.
Certainly not as data rich is, say, the self-driving car industry,
as we hope to be in the future.
But we're getting there.
And as we get there, we have this responsibility to only put
the most specific, well-characterized data into, those algorithms.
And I think that's where we on the side of caution.
I think that's why we have ostensibly fewer proteins in our assay,
because we're very careful about getting those assays into our, products.
And I think in many ways, that's your team, maybe not your team
before you joined, but you certainly have supported and resonated for that.
And I think customers appreciate that.
And just knowing that if we're detecting something, especially
if it's a intracellular protein or a membrane bound protein,
if we're detecting it in plasma, where it shouldn't be,
that has the potential to be an enormous opportunity
for discovery, by customers that are seeing it there.
And so our detection or our lack of detection
should reflect, I think, true biology.
And I think that's our messaging Sarantis at meetings.
Yeah.
So JP Morgan, we just had JP Morgan I think the messaging at JP Morgan
or the take homes that I heard there were, essentially that these companies
are in many of the pharma companies are presenting,
they're moving into a growth phase.
I think we've had two years of challenges and funding and,
and pullback and contraction and, and caution
and I think there's this this bullish opportunity with Suisse,
some uncertainty around the political climate
and the change in leadership here in the US.
But some optimism.
And it just felt very buoyant there.
And then we have right around the corner the Precision
Medicine World Conference, which is founded by Tal Bahar.
And they're really building on what that momentum,
felt like at
JPMorgan or around opportunity and Vision in order to take action.
And so to really foster, an environment
of partnership, in this precision medicine space.
So I think that's, that's very exciting.
And Jenny will be talking a lot about reveal.
Can you just give us like a high level overview is where does reveal fit
into our product portfolio and where can people learn more about it.
Thank you, Cindy.
So again we talked about accessibility being one of our main goals.
And as part of that, we are adding,
a new product to our, discovery portfolio.
So everything that is,
detected through and sequencing,
and that is Olink Reveal.
Olink Reveal is the little sister of Explore HT.
It's an inflammation oriented panel, so curated,
the assays are curated based on cis-pQTL associations,
with a strong connection to UKB.
A very strong, inflammation focus.
As I said, it's a thousand plex panel.
So it's a good,
very good protein coverage,
of course, less depth than Explore HT.
But, you know, on the, accessibility side,
it's much more, what can I say.
It's more of a mass market product.
And the reason for that being, so we focused a lot on reducing the cost
per sample.
So the cost is actually less than $100 per sample. Wow.
Which means that it would be much easier to add this,
for other cohort studies,
where we have less funding, for example, but still,
and I think, you know, we should always, aim to add proteomics
as one tool in all the big population health studies.
So that really enabled that,
but not only cost, I would also say what is related to cost,
but I will also say something about the, perceived complexity of proteomics.
So we have
focused a lot with Olink Reveal to make it super simple.
So you should be able to just go in the lab
if you have a NGS sequencer and set it up and run it.
To get results really quickly.
So you can even run it manually or with a simple automation solution.
So no big investments to start up, but something that any genomics lab
already has.
So it's an easy, simple,
way of adding proteomics to your project.
Actually, as you say, the democratizing protein actually
at the end, right, is like a nice example of how we democratize protein.
So that's great.
Yes it is, it is.
We've been waiting for this.
This is very exciting. Congratulations.
I know it was a long trip for your team, and a lot of other teams.
Congratulations.
It's a really great tool. Yes.
No, it's been, it's a project that has been ongoing for quite some time
at R&D and we're super proud of this.
And really required a lot of data analysis of the data that are out there
that are publicly available where we're allowed to go in and play with
and see what are the ones that have the highest disease associations,
what seem most promising for having future disease associations,
where these cohorts just haven't been able to afford to get into proteomics.
So I think this will offer quick publications.
And I think tracking the publications in review will be an exciting,
time to see labs
doing proteomics that have never even ventured in and then,
of course, the opportunity to validate orthogonally with mass spec
I think will be, also amazing.
That's a great point.
And I
think the choice of inflammation is really crucial
because inflammation, as all of us know, is connected to our disease almost.
And that offers a possibility from different types of researchers
for different disease areas to explore proteomics finally.
That's a great tool. Yeah.
I mean even in Alzheimer's disease, right.
Where there are clearly endotypes and some of them are associated
with information and some of them are not.
Being able to stratify
those patients in advance of clinical trials, for example, might be
some application.
I know that several pharma have reported that, and Chris Whelan talked about this,
but they have been able to do post
clinical trial proteomics on Explore HT, which does require automation.
So that's 5400 proteins
over 5400 proteins using a next generation sequencer.
And folks are seeing stratification of these,
of these disease areas after the clinical trial.
And they're seeing that these different endo types of this disease
are, are responding differently, to the treatment.
And I think that
is laying some amazing groundwork.
I think it will help a lot for
biomarkers. It surrogates biomarkers.
That would be really a great tool for following protein biomarkers
really closely.
And I have a question actually for both of you.
I think we’re discussing about now.
We discuss about tools that we’re developing now
with a perspective in the future But how do you see the future?
What do you see the challenges
and the wins we may have from the proteomics lab in the future?
I think Jenny first.
Know, yeah. This would be a great wrap up question.
I love this this is wonderful.
Yeah.
No, I, I would say I mean for the future, I think it's going to be
or it would have to be, a much more focus
on combining different data sets.
So again, coming back
to what we talked about with, the focus on machine learning and so on.
So I think we we're going to see much more,
support to combine proteomics, genomics, transcriptomics data
with, disease genotyping, for example, we're going to see much more regarding,
predictive power, on proteomics.
And obviously, how is that translated
into, clinical proteomics.
So it's going to be I think we're going to see,
you know, also just on the first UKB study, we've already seen that happening.
That you're identifying these
really nice protein signatures with a very strong
predictive power early on to say, you know,
if this patient will actually
get a certain disease several years in advance.
So I think it's going to be like, you know, from this discovery
to this more, clinical applications that's going to happen quickly now.
We already had talked about how much has happened in a few years time.
Right.
So and just looking forward and then in five years,
I can't even imagine, you know, what we're going to do.
but I think it's going to go like, you know, it's going to be more multiomics.
It's going to be much more support for clinical proteomics.
And of course, we as a supplier, have a responsibility
to help that to happen.
Great, great.
Cindy, what do you think?
What is the future?
Well, I'll piggyback on something Jenny said.
So the the idea of being able to predict disease
many years in advance of getting disease.
I mean, that was really hot news when Keren Papier
and Ruth Travis and Karl Smith-Byrne and Josh Atkins, their paper came out.
There's, you know, seven plus years, it was a median of 12 years, 12 years
in many cancers of being able to predict disease.
And of course, those are those are many of those predictive of genetic,
dispositions of folks that
are more likely to get disease, not necessarily that they actually have
the disease on board, although they also time
stratified to be able to get at a little bit of the detail there.
And I just saw they had a preprint on prostate
cancer that characterize some of the pathways in the immune system
that are predictive of a likelihood of getting prostate cancer.
So I can't wait for that to come out in publication.
So that brings up, the recent
announcement around us running the entire UK Biobank.
That's 600,000 samples.
That's 500,000 individual with 100,000 repeat samples at a 15 year mark.
Is my understanding,
being able to see that across
all of the diseases that are represented within the UK Biobank.
And some of them longitudinal also, Cindy, some of them
also longitudinal also followed, right? If I’m not mistaken.
That's the longitudinal component,
is the one that over the 100,000 that are followed up at 15 years.
Yeah. So and many of those have imaging data.
Right. That's great.
And outcome data. Right.
So being able to characterize that,
that set of samples which have around 8,000
African diaspora samples, have around 8,000 South Asian samples.
These are diverse, sets of samples that, as Rory Collins
says, aren't enough diversity for us to really characterize everything.
But across the entire UK Biobank, we do, you know, effectively
have longitudinal representation, because if you get enough samples, you get
folks that are in different stages of disease.
So though it isn't longitudinal in an individual, it can be, you know,
by being cross-sectional and large enough in size can represent
some of the longitudinal aspects of
disease progression.
So that's what I'm really looking forward to.
And I expect those data to be published around 2027.
Which means that that the world will have access to
that international resource,
which is some of the and we talk about it as the UK Biobank,
but it is the internationally access to UK Biobank.
So it's an exciting time.
And just to add to that the statistical power of 600,000 samples,
would, you know, imagine what that would mean for understanding
rare diseases, for example, which hasn't been possible, really.
I mean, I know, we had that you could see that also in the, in the sort of smaller
UKB, set from before, but with very few samples.
So I think that is also something that is extremely important.
Already with these, the first papers that we have or the small sample size
that you mentioned, Jenny.
Yeah, it's a small one.
I mean it we were able to see there are great papers
from Claudia Langenberg, that we had improvement on diagnosis of disease.
Even better than clinical outcomes sometimes.
And that was really impressive.
Well, it was really amazing for the first time to see such a thing.
Right.
Imagine that was tenfold more sample size.
What's going to happen. That more diseases, right.
More representation of those diseases. Exactly.
And ability to
you know, propose these protein scores that that
that will improve any over anything a doctor has available to them today.
Yeah. This is yeah it's a beautiful time.
And of course we are biased by our protein
excitement.
But yeah, we're happy to be a part of it.
So with that, I will wrap up this episode of, Proteomics in Proximity.
We will, as we all mentioned, we will be at Precision
Medicine World Conference in Santa Clara.
That's February 5th through the 7th.
We will have a booth.
Our booth
will be near the stage for track three.
It'll be between track three and track four in Hall C,
and very close to a little networking station.
So reach out on LinkedIn, reach out to me.
Reach out to Sarantis
If you want to set up meetings
with our team, I will be there in person
and would be excited to talk to folks that are there.
Would be great. That would be a great event.
Really? Yeah. I wish you were here.
I wish I was there, but I’m looking forward to hear your feedbacks.
I’m sure you have great feedback from that.
Yeah, we could do an episode live.
Yeah, that would be great idea. Great idea.
All right.
Thank you Jenny. Thank you for tuning in. Yes.
Thank you Jenny, thank you.
Thank you so much. Great to have you.
Well, that wraps
up this episode of Proteomics in Proximity.
Huge thanks to our guests and authors of such impactful publications.
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