Welcome to the Proteomics in Proximity
podcast
Where your cohost Cindy Lawley and
Sarantis Chlamydas from OLink Proteomics talk
about the intersection of proteomics with
genomics for drug target discovery, host,
Cindy and Sarantis.
Hello, everyone
Welcome back to Proteomics in Proximity
I'm, one of your cohosts, Cindy Lawley,
and I'm here with my cohost, Sarantis
Chlamydas
Sarantis, why don't you tell people about
our guests today?
Thank you, Cindy
Happy to be here today and present, to
great scientists from Helmholtz Munich,
Dr. Stefanie Hauck, the head of the core
facility, Metabolomics and Proteomics, and
Dr. Gabi Kastenmüller, head of
research group of the Computational Health Center in
Munich
And, nice to have you here, Gabi and
Stefanie
I would like a little bit to know more
about your scientific background and your
interest in the in the research in the
area of proteomics and metabolomics
Thank you
Thank you very much.
Yeah
Okay
So thanks a lot for having me, for
inviting me
It's quite an honor to be guest in this
podcast
Well, my research backgrounds, I am a
biologist, and I actually did my PhD in cell
biology in retina
So it's quite far off to what I'm doing
today, but not really because, very early
on, even during my early study phases, I
came into contact with mass spectrometry.
And that got me kind of infected, so to
say
So I was, pursuing my research questions
always with methods in, proteomic profiling
and also in this cell biology project in
the retina where I did mass spectrometry on
glial cells
And, of course, ever since, I broadened
the applications and moved more and more
towards a technology-driven research.
And that's where I'm now
So what's heading this, analytical
platform where we do still use mostly mass
spectrometry, but have also acquired
affinity proteomics already, I think, five years
ago.
Which makes you cutting edge in that space
for sure
And I and and and before we move on to
you, Gabi, I will say I'm glad that you're
happy to be here because we we would blame
it on Karsten Suhre if you weren't happy
to be here
It was his suggestion to bring both of you
on, and I've been looking forward to it,
very much
So so, Gabi, please tell us a little bit
more.
Yeah
Hi, Cindy
Hi, Sarantis,
Thank you for having me
So, yeah, my background is in computer
science, and chemistry, actually
So I'm chemist for training and also
computer scientists for training
And came into bioinformatics during my PhD
phase
And it was actually Karsten Suhre, who
brought me into metabolomics, which, given my
my background is the perfect thing.
A lot of data and, back to chemistry at
least at least a bit.
So, yeah
So during my post-doc phase that I did
with Karsten Suhre I was brought into metabolomics, not so
much proteomics yet, but, what we are
interested in is really to understand the role
of metabolism in health and disease
And of course, when you think about
metabolism it's not only the metabolites as the
products or players in a metabolism but
it's a lot also about proteins, of course,
and all the interactions you see between
all these different layers including
genetics that's, of course also coming in.
And in my group, we really try to bring
together all these different layers to help
us understand what's going on in
metabolism, in a more systematic way on the
molecular layer so that we can use all
these new techniques and information.
I love it
I'm just gonna kind of take a step back
and give some definitions of a few terms
to give context
So we think of the central dogma of DNA
makes RNA makes proteins
And lots of people talk about multiomics
being the DNA measurements, maybe arrays or
DNA sequencing, and then RNA data being
another layer, as you talk about, Gabi
And then proteins are another layer, and
then metabolites are even yet another
layer
So I just wanted to layer that on because
we we talk a lot about proteomics,
obviously, and genetics and just, the
complexity of bringing together all these
layers is is, there's so much, richness
there, but it's not easy to work with
multiple layers and to integrate these
data.
Is is there I guess, Gabi, I'd ask you,
about your your informatics background or
bioinformatics background
Where do you see this going?
What are the challenges?
What how do we accelerate our
understanding of systems?
You know, a lot of people call this
systems biology of systems, to advance precision
medicine or individualized medicine,
whatever we wanna call it.
Yeah
One one problem I see is that we have
usually we don't have these data all in the
same sample or in the same subject
individual organism, right?
We have a lot of pieces here, pieces
there, some single cell data is coming now,
right, for transcriptomics and so on
We have large cohorts where we have
proteomics and metabolomics in, but it's it's,
not, the situation where we have, like, a
UK biobank scale of data on each tissue,
each, fluid, each person.
So when we talked about data integration
in the past and 'Omics data integration, it
was mostly thought like you need all these
layers in the very same samples to make
sense out of it
But what Yeah
It's still very complicated to get that,
especially in the human case where the
excesses accessibility to all these
tissues that would be of most interest are,
yeah, it's very complicated, right?
You cannot just access brain tissue
easily
And that's true for most of the other
tissues as well
So we have to work with surrogates always
The big cohorts help us, but we have to
combine all these results at some point, to
make sense out of it as much as we can at
the moment.
That's that's great
And I would like to ask to come back to
you, Stefanie, a little bit more technical
in proteomics
What is the biggest challenge... for
proteomics to be included in this
proteomics world?
What is according to you, because you have
worked with a lot of proteomics,
platforms, and what is your feeling?
How is the biggest challenge there?
Yeah
Come, proteomics is kind of developing
very fast, and challenges, of course, are
still, to keep the balance between high
throughput and still yet high sensitivity
and, robust quantification
So we want to have at least those three
parameters, at a very high level
And that is, more and more successfully
done in proteomics nowadays.
So there's a lot of development with
increased sensitivity, from the machines, so
the mass spectrometers
have a lot of increased throughput,
methods
So we have meanwhile, routines which could
measure one hundred twenty samples per
day or something like that
So connected with robotics in the front
end for sample preparation, that is quite
something, and we are moving to what we
want.
However, there's this one huge challenge
which still remains, and that is basically
studying the proteome of plasma or serum
So these are the two body fluids which are
the most dreaded sample for any mass
spectrometers because the, protein
abundance is so, disbalanced in these samples
So one has, like, one protein making up
for more than fifty percent of the total
abundance, and it goes like that.
So a total of twenty only twenty high
abundant proteins make up more than ninety
nine percent of the protein abundance
So it's a very disbalanced proteome
And, in tissues, that's not so much of a
problem, but the challenge still remains
for plasma and serum
And this is where, at this point, of
technology development, mass spectrometry
methods fall a bit short because they
cannot, deliver the required sensitivity.
They can, of course, deliver the accurate
quantification
And as I said, also the throughput, but
the sensitivity here is lagging behind
And that's where kind of complementary
methods now come in more and more
And this is mostly the affinity based
methods because there you can kind of target
specific proteins and then include an
amplification step to increase the signal to
kind of fish out the low abundant
proteins.
So but, taken together, all these
approaches, proteomics is kind of coming of age
And now, hopefully, we are entering a,
well, after the genetic and the genomics
phase, we are hopefully entering a
proteomics phase in discoveries.
That's great
That's great to hear
That's, to hear
You mentioned about the plasma and the
serum
Have you seen also a need from other type
of matrices?
I know this got done great work with,
dried blood spots, but have you seen also
other type of matrices that it's
emerging in the research areas.
Yes
Dried blood spots, for example, as you
mentioned, that's for sure a matrix to look out
for because that's, of course, something
that's a very hot topic because it would
enable home sampling
So everybody so in a bright future where
proteomics is kind of, well, very important
parameter to discover diseases early on
One could envision that everybody's kind
of screening his or her own blood once a
week or something like that at home.
And then you would rather micro sample
that on something equivalent to dry blood
spots
And, also their measurements are well
feasible
Other matrices, of course, there's other
body fluids
One has to take, really look carefully
into that, what the gain is
Yeah, one could also type other body
fluids, but every body fluid comes with its
challenge.
With its challenge
Yeah
That's true.
That's where the technology expertise
comes in
I think this area of discovery and
certainly leveraging, I think, mass I love this
message that there's there's this
complementarity between mass spec methods that are
that are arguably the gold standard, and
will continue to be that essentially,
pivotal to moving things to the clinic,
certainly, historically and in the future
But the complementarity of that with,
affinity based methods, these methods like
Olink that that can fish out or pull
these, proteins out of solution that are more
low abundant.
I'm wondering about the less sexy side
that I think, Gabi, you've you both of you
have been involved in around, driving the
ability to have data publicly available as
much as possible, so that it can be
crowdsourced, in the analysis
I think that that you've both been big
advocates for this, and I just wonder if we
can talk about the importance of that
I say it's less sexy because sometimes
it's a requirement of publication.
It does it's it's, it's work to maintain,
databases that are publicly, accessed, but
it's absolutely such an essential part of
the scientific method
And I wonder if you might comment a little
bit about the importance of that and
your passion, what I think is is a
passion, around that that I share.
Yeah, I think that's, so there is so much
money invested producing all these
different data that it's really the best
you can do to really leverage each and
every part of it to share the data
And of course when we are talking about
comparing different platforms, it's not so
much about comparing but as you say, using
the complementarity of these
measurements, right?
They are telling they have an overlap
telling you the same thing reassuring what
with one platform and the other
But there are also things that you only
see in one, but not the other
That does not mean that they are wrong,
but it can really, explain a lot and you can
learn a lot about, also a certain platform
by comparing it
We also have that and use that pathway in
metabolomics, right?
Where you can, if you compare the
measurements between NMR and MS you can really
tease out new signals, interesting signals in an NMR and
also in MS that you would wouldn't have seen
otherwise.
Now, when when you ask me about sharing of
course there are some issues also with
sharing of data in many of these older
cohorts
It's a problem
It's a problem of data protection
It's a problem of the consent that people
gave, right, not to share their data and I
think that has to be respected
So I think it's equally important to share
the results.
We have a lot of results, right?
That's almost equally as much as we have
data, we have at the end associations, be
it associations with genetic variants or
diseases that also do not fit in a
publication in a format like a
publication
And so very often in the past things ended
up interesting associations just ended up
in in supplements, PDFs that you cannot
query, right?
And, yeah, in our research we would argue
that each and every of these associations
between let's say one protein and genetic
variants in one gene that is a story on
its own and can be a story and important
information also for a person interested
specifically in that gene, specifically in
that protein, specifically in a
metabolite
And if a researcher from the more
experimental field cannot access this knowledge,
then it's also a problem, right?
We try to, to also share the results we
have
So these long lists of associations and we
try to share that in a way that people
who do not have bioinformatic training, or
the the capacity also, right, of of of
downloading, like, gigabytes, terabytes of
of data to look into one gene or one
protein or one metabolite
So we try to make it accessible through
web servers where where we enable people to
query that way, right, their favorite
gene, their favorite protein.
We will share we will share this link
later
Alright?
I think, is a great with
people that can go and I'm I'm amazed with
this amount of data that there there are
even though for me that I'm not a
bioinformatician
I get a lot of important information
I think this is it's important to make
democratic
We're saying it's only we democratize
data
I think this is actually what we're doing
there, democratize data
A lot of Pharma, they will appreciate this
way of seeing data nowadays, you know
And, yeah, it will be great, guys
I really encourage you to play in with
this portal
Go through this portal because you will
learn a lot, about how, multiOmics will be
integrated actually in a very easy way.
And, yes, please.
Can I just layer yeah?
I just wanna layer onto that, Sarantis,
and say, so so Gabi is a coauthor on, the a
Nature paper that came out in October that
we've talked about before
We certainly talked to to, to, Chris
Whelan in a separate podcast episode about the
work before that Nature paper came out
But the the data are individual level data
that are shared publicly on the UK
Biobank Research Analysis Platform
Now researchers can apply to get access to
those individual level data and do their
own work with those data that includes
proteomic, genomic, much of it exome
sequencing now, and, clinical, markers,
phenotypic data.
But the, the associations between the
genetics, as Gabi describes, those those, you
know, genotypes and protein levels, those
associations have been run-in the past
There's no need to rerun those analyses
And so the beauty of these resources is
that you don't have to rerun them
You can you can look at the different p-
values
You can adjust your p-values, see which
ones are significant according to the, the
standards of of, multiple tests that you
want to, use and play around with those
those metadata.
And as Sarantis says, we will post the link
to those data, in the, in the show notes
because we highly recommend that folks go
play with those data
And and one example of a way I use it,
I'll just give one more use case, and then
I'm gonna give it back to Sarantis
But but oftentimes, if I see a signal
that's been identified by a customer in in
plasma, say there's three or four
proteins, I'll often go and look at what are the
associations with those same proteins from
those metadata.
So you can, you can download it by protein
and see all the genetic associations with
that protein from the that massive dataset
of over fifty four thousand, UK Biobank
participants
So it's, it's a it's an enormously helpful
resource
I'm not a bioinformaticist either, and I,
I use it, quite frequently and used to
have to use Excel tables
And now I can go, go directly to the
source
Thanks for letting me, I have I have I
mean, it's an amazing topic, and I have a lot
of questions.
And, I mean, I also like to ask you, we
are we are hearing a lot about artificial
intelligence, machine learning, you know,
this is a lot of papers, a lot of
discussion about that
We know there's also a lot of buzzword
around this
How close we are to create, let's say,
accurate models according how close or how
far we are to create accurate models for
prediction, for example, based on
artificial intelligence updates?
Are we close?
Or what is your what is your thoughts
about that?
So, I think that depends a lot on which
disease we are talking about and what we
really want to predict and how much the
prediction is on a certain individual
So, I know there is a real hype at the
moment with artificial intelligence
So, what we can use more on that layer of
'omics data at the moment, I would still
call it machine learning and that's where
these prediction models you are referring
to are also coming out more.
So, I think that we are close in that
sense that having all these measurements now
for so many people with all that
additional information, I'm sure we can tease out,
clinically relevant ones and that we can
follow-up
But it's not so at least that's my
opinion
It it's not, the same type of model that
people talk about when they are now
fascinated with that large language models
and that generative AI where you, what
you see when looking into the media with
the with the with the all the images.
Right?
Where where new images are created from
the knowledge, from the models you have
So, these models have been built on much
more data
So images are out there in, yeah, you have
much more to learn on and it's about
text
In biology, I think we are not quite
there
People try to use these the same
techniques now and I think it won't take long and
to also see successes there, but it's it's
it's not the same as what you had been.
It's great
It's great.
Yeah
And if I may follow I'm sorry.
Yeah, please.
Yeah
I would like to directly follow-up on this
because I totally agree with Gabi, and, I
feel very comfortable that you will
suggest to call it machine learning before we,
before we involve artificial intelligence
So and and I I just wanna make the point,
in the end, it's not about discovery, but
about application
So in the end, it will be individual
people who who want a clinically accurate
decision.
And if he involves all the new markers for
actually more accurately predict or
diagnose some condition, what we will need
is to perform a lot of groundwork to get
this, actionable in a clinical setting
So it's really and there we have to go
I mean, we can aim high with machine
learning, artificial intelligence, and all
these visionary applications.
But in the end, we have to come down again
and do the groundwork to actually make
the markers accessible in a clinical
context
And there, we still have a long way to go
I mean, there is, of course, clinical
chemistry
There are many assays already in the
clinics
But most amazingly, many of them are in
the clinic since more than forty years
unchanged
So there is a lot to be added on.
They they provide very good value, but
there is a lot of room for actually
translating biomarkers into the clinics
And but you, you know, you realize the
level of groundwork you have to do when you
just start with thinking about SOPs,
developing standard methods
We have been involved in such an
initiative in Germany where mass spec is meant to be
brought to the clinics on a long term
goal.
So project mass spec for proteins, I have
to say, because metabolites are already
there in the clinics, metabolite
measurements
So this initiative, it's a Germany wide
initiative
It's funded by the, BMBF
So it's MS courses
And there, we actually team up with the
four different course to perform, ring
trials
And there, we do the total groundwork.
So just look at what we can see with
mass spec, what can we see in every sample from
every lab, irrespective of the methods of
the machines or else
So there is a lot of groundwork still to
do
So and we also, of course, explore the
affinity methods in this respect to see where
we can go
I think where where all these new learning
methods, artificial intelligence can help
us is getting ideas, getting hypotheses.
But really, what what we really need to go
to the clinic with anything, right, is
understand what it means.
And if I am correct Stefanie, the
biggest problem for the clinical
biomarkers because apparently there's a
need for clinical biomarkers and it's an
emerging need
And there are not so many out there or
they're not so many well validated need to be
expanded, let's say
It's more the let's say, the SOPs, it's
more the the background work that (is needed)
Right?
Rather than the technology or also yeah.
I mean, there's also such simple things
So what we do in the explorative research
setting, we always use relative
quantification
So you throw in comparative data from,
like, two hundred, one thousand or with the
UK Biobank, even fifty thousand samples
And then you have, like, arbitrary units
So it's relative quantifications
But that doesn't tell you in the end if an
individual comes in, where is he or she
in the scale.
So you need to have absolute
quantification
This is the groundwork for the clinics,
and we are not there
But one can go there
And for the markers, which, deserve it, so
to say, which are well validated in all
these discovery cohorts, one should go
there.
Yeah
Yeah
I I think bringing I think characterizing
I think your point about characterizing
the signatures
I I will say one of the researchers was on
stage at a precision medicine meeting,
and he said, somebody was talking about
how they've got, artificial intelligence to
extrapolate an understanding of African
diaspora populations so that they can do
better at treating those populations with
signatures
So they were using the point that they
have a small amount of data on these
populations, but they can extrapolate.
And I remember the response was, I prefer
real intelligence over artificial
intelligence
In that case, we need we need more data to
understand the diversity to feed into the
future whereby we are then leveraging that
information to then go... It's almost like
we have to go big and then we have to go
small
And when we go small, what I'm hearing is
they have to be exact quant approaches
Right?
And so I think talking to folks like you
to help us understand that path to the
clinic is critical because and just to
make our lawyers happy, Olink is a research
use only technology, but certainly there
are customers that are leveraging it to
build these midplex twenty protein assays.
You know, Octave Bioscience is a great
example, where they're exact quant and
they're able to be clinically validated by
independent evaluation
So just to give make sure our lawyers are
happy with us.
That's now
Absolutely
That's a good point
So I have also yeah
Please
Yes
Please.
I I I I like the idea to at at some point
bring also more complex signatures into
the clinic where not only it's not only
one protein or one metabolite but really
signatures where we can capture
interactions better, for example, or get a clearer
picture of different mechanisms playing a
role in a certain participant in a certain
patient or or or a person going to the
doctor
Right?
And but that's we are far away from this,
I I think because we we need the
understanding how things are connected.
And that's the phase where I see, what we
do at the moment
Right?
So we try to understand how things are
connected, like, integrating all these big
screens, all the information that we get
from these big screens.
So I actually can we can we just focus
now?
Because I see, you know, what you're
providing there as a as a service, is
essentially a rising tide that's lifting
all boats
Right?
So you've got your agnostic tech
technology
You're providing the right guidance for
different researchers that are using
different technologies for what might help
them get to where they need to be
Can we talk a little bit about what you
offer, the kind of services you offer, you
know, who might benefit from this?
Are there is there an opportunity to,
collaborate with you all?
What what is there something to be said
about about that?
Yeah
Sure
I mean, my analytical platform is totally
open to collaboration, to academia, and,
well, even companies if they are
interested
But we are, of course, mainly academic
So and what we offer is actually,
solutions
So a little bit tailored solution for
scientific questions by using proteomics and
metabolomics techniques
So we do, of course, predesigned essays.
I would put Olink in the predesigned
portfolio because it's basically a targeted
method, which is, just measuring what is
in the assay
And there is different variants
We offer them all
And, but we also have, we could also, in
metabolomics, for example, design, new
methods to discover certain metabolites if
needed
So we are totally flexible there.
And then proteomics, that's also something
we regularly do with mass spec
So there's all the portfolio like
interaction proteomics, phospho-proteomics,
extracellular matrix profiling, you name
it
We we most likely also have it or can at
least make connections to someone who is
expert in that
It's a very broad field and it's taking
it's quite a challenge to stay afloat, so to
say
But, yeah.
And we are accessible And it Yeah.
To Yep
In order to lay this foundation that we're
talking about, it takes funding
And I know that you're, you know, you have
you are up you apply for funding
I know you're on many publications
Both of you are, of the the direct
research in different disease areas
I think you're you're probably a a, jack
of many trades in that in that regard
But I think I think the point is we need
to we need to drive funding to, to maintain
these valuable resources and expertise.
I think the general thing for basic
research
Like, I think we have to we have to fund
basic research in order to arrive to to to
to translate to Pharma
We need to to help them on basic research
That's for sure
I have also my usual question if I'm
allowed, Stefanie
It was always had in my mind about the
proteomics, the plasma proteomics, and when
you correlate back to tissues
Right?
How many proteomics proteins in the plasma
you are correlating with tissue or tissue
specific in percentage?
Because I I don't know.
This is a very good question
I mean, you could you could even go easy
and ask the question, how many proteins are
in plasma?
And I think nobody can answer this Yeah
At this point
Because, I mean, if you haven't seen it,
that the reason might not be that it's not
there, but that just the other stuff is
covering it
So I I would suspect that every protein
could theoretically be present in plasma at
some time point on some occasion because
I mean plasma or the blood is touching
everything in our body.
So it's not reasonable to assume that, I
mean, if any cell on the way is just
degrading, then every protein from the
cell could theoretically be found in plasma
So I think this is a philosophical
question.
It's like a cell type decomposition
Right?
It's like you can and and I think we've
been trying to do that with RNA Seq in blood
for a long time
Yeah.
But, I mean, that's also the the big, the
big opportunity in plasma because it will
report on any damage that is in our body,
ongoing
So that's basically why this is such a
promising, sample despite being very
challenging
So, yeah.
Yeah, I think from what would can what
plasma proteomics can show quite a bit also
in healthy cases, right, is what, the
immune related, signals are about or the
status is for a person
So in that... inflammation...
metabolomics and proteomics is really
highly complementary, when when you think
about blood metabolomics and and
proteomics because, yeah, from in in blood, for the
metabolites, you see a big mixture of what
coming from all the different tissues,
liver, kidney, all of them muscle, all of
them are metabolically very active.
And for metabolites, it's really the
the medium which used for the transport of
these things from one organ to the other
But the readout that you get from plasma
proteomics, is is really very much
complementary to that because you need the
immune system, basically.
Gabi, can you in the, healthy,
population.
Individuals.
Yeah
And understanding that transition to
disease
Gabi, we don't get a lot of people to
talk about metabolomics on here
I'd love to just talk a little bit about,
I think that I think of metabolites as,
like, the, the money that, that the
microbiome pays to the body for rent, for
renting space in the gut or wherever, you
know, microbiomes are unique
Is that how you think of it, or can you
fix my way of thinking about it?
I just find metabolites are giving us
signals that are beyond our organs, but are
are also part of this community that we
are just starting to understand with
sequencing approaches that allow us to
sequence things that we haven't had to grow
in a petri dish.
Absolutely
So so it's a chemical, the chemical
language, that is used between microbiome and
and the cells of our body, right, the
human cells of our body
So I also see that like that
But sometimes I feel that the, it's a bit
over over rotation of the of the
microbiome also happening
So it's it's in waves.
Right?
I have seen another wave, an earlier wave
of the microbiome
Before there is, one now or has been, now
I'm not saying it's not important
I think it's tremendously important but
and a lot goes via metabolites but also via
imprinting the immune system
Right?
So these are parts we have to cope with
and, in part fight against them but also use
them
But, it's not so the metabolites in blood
are definitely not, only about what what
the microbiome, gives into into the soup,
into the play.
It's it's really, also, transporting what
is produced in the liver to get it to the
muscles where it's needed and and so on
So that's what we see more at least in the
metabolites as they are measured now
But I know it's it's not about metabolites today...
It's more about...
But it is all part of like, these are
intermediate phenotypes that are helping
amplify our ability to understand real
time health
Right?
So I do I do appreciate them and the
complexity of integrating proteomics and
metabolomics
So thanks for indulging me.
Can I I'm allowed in that?
I mean, because it's really interesting,
the topic, you know, the proteomics
is fascinating
You know?
It's and I also also wondered, are there
any protein complexes that survive
in plasma under these conditions?
Or have you seen Stefanie?
Have you isolated some protein complexes
that, are are staying there and they're
functional?
Yeah
Well, the most prominent protein complex,
if you wanna call it like that, in plasma
is, of course, extracellular vesicles
I mean, this compartment is rich in
vesicles, all kind of vesicles, and they are
getting a lot of attention nowadays
We also look a little bit into this
It is a bit, so there is a big chance to
also... vesicles go into plasma by several
means.
Also, of course, from donor organs that
shed those vesicles
So it might be very interesting to, pull
out organ-specific vesicles and, find some
markers in there or with them
But, I think there is yet another
challenge to cope with, but I'm not really very
well in this field
Well, I'm knowledgeable in this field.
But if you freeze the samples, you most
likely also destroy parts of these vesicles
So and that, of course, poses a serious
limitation because typically, the samples
are stored frozen because that's what
preserves them best, but not in the case of
vesicles
So, yeah, that's all I can say.
So so my some of my tendency, my tendency
and conservation is always really
important for proteomics, but also for
other any other, let's say, metabolome.
Yeah
For metabolomics even more
I mean, that's degrading.
Yeah
Yeah.
Super fast
So yeah.
And a lot of protocols there for different
type of matrices, for different type of
analytes
Right?
And that's that's also a big challenge
because there's not a new unified protocol,
let's say, for everything
That would be amazing
Yeah
That's super.
I can't imagine talking so broadly across
so many topics with anyone else
I mean, we have recovered a lot of
different areas, and I think, like I said, I
think you both are you may have started
out in one area, but you've become very
broad in your understanding and your,
importance as a collaborator to many
researchers
So I I will I looked in our database of
Olink publications, and I'll say, Helmholtz
has been prominent in over a dozen
publications just in the last few years.
So it's, it's exciting
And and all of that, I think, required
some of your involvement
Just in our last couple of minutes, I'd
love to let each of you, say a last a
last a last word
And I also wanted to acknowledge Matthias
Arnold, who I think was pivotal in in,
helping upload some of those UK Biobank
data because he's been very generous in
answering questions about those data with
our customers
So I just wanted to give a shout out
So if you wanna give any shout outs,
although it's always dangerous because you
might forget someone.
But, but, please, Stefanie, anything
anything any last words from you?
Yeah
Okay
I may, just follow-up on what I started to
elaborate a little bit, before that the
groundwork towards the clinics, it's
really something which I feel has to be tackled
now
And at this point, I would like to thank
all the great collaborators in the
CLINSPECT-M Consortium in the Munich area
and across the MS courses consortium
consortia
So we have a lot of cool interactions
there.
And, we have our first paper out, and I'm
looking forward to a bright future with
bringing mass spec or the alternative
methods
But in any case, proteins into the clinic
So it's a long time
We'll put a link to that we'll put a link
to that publication.
Yeah
That sounds great
And Gabi?
Yeah
We'd like to thank Ben (Sun) and Chris (Whelan), who
brought us into that big proteomics project
from UK Biobank
It was really a great collaboration and of
course we have worked on proteomics with
Karsten Suhre before and hopefully will do in
in in the future
And so, yeah, these are really big
scientists, having broad vision.
And, of course, I also, like to thank my
the people in my group, who who did that
Right?
So it's Nick, Maria, and, of course,
Matthias
He might be a good candidate for a new
podcast.
Let's do it
I love it.
Yeah
Of course, in our case, we did also some
of the proteomics work in collaboration
with Claudia Langenberg
So if she also brought us into some of her
big studies, So as that proteomics is not
the core of our scientific work, but we
are so convinced that we should bring all
these big 'omics screens to the people,
to the experimentalists, to the biologists
that we are always happy to be on board,
for making this service and also improving
the service, hopefully in the future
together with all these great collaborators.
Yeah
And to the informatics scientists because
they're gonna build us these algorithms
that will help us get a a better
understanding of all of these data layers
So thank you so much, Gabi
And then and then Sarantis.
No
I will say only, like, they say
data sharing is caring, right, at the end,
and, it's important to share and important
to share data, especially nowadays with
all of this big data available
And this will, bring science and, blood
development processes upfront
And looking forward for more and looking
more for more data and more, more analysis
Thank you very much for coming
You're actually because it was a great it
was a great time for us
Thank you.
Yeah
That was fantastic
And so just for our listeners, if you
enjoy this content, please share with someone
you think might be interested
If you have feedback for us or guidance
for future episodes, please email us at pip@olink.com
which is, stands for
proteomics in proximity
So thanks, Gabi and Stefanie
I am so happy to have you here, and let's
see how long it takes us to actually get
Matthias, scheduled to this podcast
That'll be fun
I hope he agrees to to chat about some of
the the aspects he's passionate about.
So thanks, everyone.
Thank you.
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 at olink dot com.