Proteomics in Proximity

All about Olink Flex: https://olink.com/flex

Olink Insight (where you can build a 21-plex Olink Flex panel from 200 proteins): https://insight.olink.com

Sweden’s Science for Life laboratory website: https://www.scilifelab.se/about-us/

In case you were wondering, Proteomics in Proximity refers to the principle underlying Olink Proteomics assay technology called the Proximity Extension Assay (PEA), and more information about the assay and how it works can be found here.

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What is Proteomics in Proximity?

Proteomics in Proximity discusses 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. Co-hosted by Olink's Dale Yuzuki, Cindy Lawley and Sarantis Chlamydas.

Welcome to the

Proteomics in Proximity podcast, where

your co hosts, Dale Yuzuki, Cindy

Lawley and Sarantis Chlamydis from Olink

Proteomics talk about the intersection of

Proteomics with genomics for drug target

discovery, the application of

proteomics to reveal disease

biomarkers, and current trends in using

proteomics to unlock

biological mechanisms. Here we have

your hosts, Dale Cindy and

Sarantis. Thank you for joining

Proteomics in proximity. I'm your host, Dale

Yuzuki, with my co host.

Good morning. Good morning,

Katerina. Good morning, Dale. And

today we have as a guest with us

Katarina Hornaeus. She

is currently a product manager with Olink

Proteomics. And Katarina, if you can tell us

a little bit about your background

first. I've found

that you're really interesting in terms of

you've been with Olink for many years,

but... maybe we start with sort of your

education and then take it from there.

Yeah,

I moved to

Uppsala, uh, early 2000,

and started

studying molecular biology

actually, uh, at the Swedish

uh, agricultural university, uh,

because I was so into

veterinary, um,

medicine and

animals, but

soon realized that, ah, I was

more into the tiny molecules

rather than the

actual, ah, animal.

And you grew up in

Sweden and so did you grow up on a

farm with animals? No, I

didn't. I grew up in Stockholm.

Uh, okay. City girl. That's my

four first years in city center of

Stockholm. Um, I see. But nonetheless

interested in animals. I mean,

Sweden certainly is an agrarian

place. And how many of my colleagues at

Olink really like horses, for

example? And of

course, I don't

have much exposure to horse culture. I don't

know, Sarantis, in Greece, it's not

something usual. I mean, there are people

that are taking care, but uh, I like

horses. But

you have to dedicate a lot. Of time

and effort, of

course. Katarina, then, so

you were at an agriculture university

and you discovered that you were more

interested in molecules. So then what did

you study there?

Um, what I studied at

so, they had actually

this bachelor, program in

biotechnology. Um,

so I started,

studying that. And then the

agricultural university is very close

to Uppsala University, where you

have all other education.

So they had some nice

collaborations. So I did some courses at

Uppsala University, some courses at

the agricultural

university.

so, yeah, they gave me

my degree in molecular

biology.

Um,

and then I did a few

years in a genetics laboratory, as

a research engineer.

Really? What kind of genetics did

you work on in that

laboratory? Then? I was back to

animals. ...so

we were

DNA typing horses.

There you go. That's great

because in Sweden you have to

DNA type your horse to get a passport for

them to make sure you have

the correct father and mother in the

passport.

They work with that. And we also,

did some,

genetic tests for

diseases that they can have.

...so you understand

then, a lot of molecular biology

techniques on the genetic side.

And then where did you go?

then I did something

completely new, so

I went, uh, to

the newly formed Science for Life

Laboratory in Sweden,

m, there was, um,

um, a

woman at Uppsala University,

who was going to start up a core

facility for Mass Spec

analysis, proteomics analysis

with Mass Spec.

Uh, so, yeah,

together we started building this

core facility for Mass Spec analysis.

Um, so then I was into

proteins. How do you see this

transition from genes to

proteins? How do you feel (about) this

transition? Actually, I, uh,

was really excited because of course, you

get some background to everything

when you study, but when you start really

working with it yourself,

you start to see completely

different pictures of everything.

And, um, I was

so excited about proteins because

that's actually what's going on in

your body, right. So you can

really

look at different, uh,

phenotypes, based on the

proteins, which you can't really

do, on the

genetic side.

As far as the Science

for Life laboratory, if I understand

correctly, this is a government effort

right, for research. And

they set up basically a couple of

institutes around the country.

Was the one in Uppsala

established the same time as the one

at the Karolinska? Is it

the KTH in

Stockholm? Yeah. So I think, at least

those Mass Spec facilities

were set up around the same

time, but they had

slightly different

angle. Uh, since

we are so close to

agricultural university, we did a lot

of studies,

from the

veterinary side of things, and

a lot of bacteria, a lot

of plants.

So, I would

say

more those kind of projects rather than

the bigger Human Project. I see. So

the bigger human project at KTH, this is

with Mathias Uhlen, right? Exactly.

And Johan Schwenk.

Oh, Johan Schwenk. And this is the

Human Proteome Atlas. Right.

That they had started around that time.

Exactly. And then I see. And then

the group up in Uppsala that you were

working on was still using proteomics,

but looking at as a part of Science

for Life laboratories, looking at other

organisms. Yeah, but we did Human

Project as well, mainly from

Uppsals University. Um, but

I thought the ones that were non human

organisms were much more

ah, interesting. Wow.

So here it is. You go back to

the non human world, right?

Yeah, exactly. But it

was mainly because

all the

projects coming in were so different.

And you work with

bacteria and then you were working with

horses, and then you were working with a

plant and. Then, yeah,

so you went from

genotyping horses to look at parentage and

lineage to looking at

horses from their MasSpec

proteomes, then

being an expert in Mass Spec, actually, for

sure, you have seen some limitations. And

then how do you see this transition with

Olink technology? How do you

see this transition from Mass Spec to

Olink? What is your experience on that?

Being like a Mass Spec

expert? I mean,

the main reason

for me quitting my

Mass Spec career was because I was

so sick of all the

maintenance that you have to do with a Mass

Spectrometer. I

can't count the hours I

was sitting there in front of that

huge machine with this liquid

chromatography system

trying to get this column in the

exact correct position, get

the pressure at the exact correct level to

not get any leakage.

And then after 2 hours you were like,

yes, I finally nailed it. And you walk up to

the office and then 1 hour later you go down

just to check that everything is fine and

then it's a leakage and you have to start

over.

There it is,

it's like a part time plumber, right?

You have to make sure the plumbing is

correct. Now that's

the first time I've ever heard of that.

Right? This is the first time we get to

that level of detail where somebody

is routinely doing this. I

don't have mass spec experience,

direct experience, but here we're talking to

somebody. You did this, you were at,

Uppsala, uh, there at the Science for Life

Laboratories for a couple of years, is that

correct? Yeah, I was there for three

years approximately.

And then,

...was that at that

point you joined Olink?

Yeah, I was

at Uppsala University. I

heard about Olink and the whole

time in different (conversations).

And looked up

Olink, found out that the company is

just across the street from where I'm

working. This company seems super

cool.

And then I

was looking for a

job that was not in the

lab because I felt that

I've been in lab for so many years, I

wanted to try something else.

And then there was this position

for, a technical support

position, at

Olink. Um oh, how cool.

And this was very early

in Olink's sort of commercial history.

What year was that? That

was 2016.

2016.

So this was at the very

beginning. Exactly.

So you were

one of the first technical support people,

is that right? Yeah, I basically

was the technical support at the

time. So we had one

technical support, we had one data

scientist. I think

ah support role is a great role.

I think all of us who have passed

all these things because you learn

the good and the bad things of your product,

the good bad things from your customer. Do

you have some anecdote from a customer at

this time that you can share with us just to

hear how people, they start to describe? No

names.

Thank you Dale.

We have so many great

stories from these early days,

but I remember one,

customer from I think she

was on Ireland,

it was a really

small project and it wasn't like,

cell lysates, nothing that we

promote that we were doing at the time.

But

anyway, they came back to us and said that,

we've run your technology

now, where we got the results from analysis

service. We find nice,

separation of our groups, but

then we wanted to validate this with another

technology.

So they had validated

or tried to validate the

results using an ELISA And,

the results were like,

all over the place.

So they were like, oh, it

is crap. We don't trust

you. Your

technology sucks. They were like,

super

upset. No, I know what that's like,

because there's so much investment. Right,

exactly. It was fee for service then.

Right. They sent samples from Ireland to

Sweden. Yeah. So a lot of work

went into it. Understood.

So we tried to solve it, but

then in the

end, we just gave up. And

we bought those ELISA kits

that they had used. We brought them

in house to R & D.

And since we had our samples, that analysis

service, we used our samples and

some additional,

samples that we had. We ran

recombinant antigens in

buffer. We made a pretty

big experiment out of this.

And what we found

out in our experiment was that the

ELISA kits were actually or one of the

ELISA kits was actually

not measuring, but it said

it should be measuring.

oh, that's great.

Here it is. You

actually had the exact

antigen. And this was when we had,

what, just like a single

Or... I think

we had around five of them at the

time.

The customer was

running several target

one particular protein.

Right. They find something

interesting. They're looking at

an ELISA, nothing's matching up.

So you're looking at one protein out of

several hundred. But you went ahead and

tried to reproduce the customer's

problem, and then you find out that

their orthogonal validation

method was incorrect.

Yes. So

what did the customer say after that?

I think they were

actually still a bit grumpy. They were

a bit disappointed, I think, that

it wasn't the way they thought it was,

but... it

generated some additional Olink

studies from that specific

place.

That's great. That's a

nice story. Yeah, that is

a great resolution. Right.

Where actually

there was a problem with the other method.

Yeah. Were there other

situations right. Where, there were

just things that were mystifying

to Olink, but then

resolved. Yeah,

I have two other

stories on. One is, um,

where we ran, like, our

first huge, big study.

So, 1000 samples. That

was like, enormous for us

at the time. It was this

important KOL (key opinion leader) from the

UK. We were like, all right,

we finally get to run a

project for this important customer, and we

really want to give them like, the best

results ever For clarity.

KOL means key

opinion leader. So we're talking about

prominent scientist, who's senior

author on papers, et cetera. Go ahead.

Exactly. Ah,

so they sent us 1000

samples. We used,

um, I think our Target 96 Inflammation

panels, sent back the data. We were like,

okay, so this is going to be great.

Finally they're going to see how great our

technology is. Um,

so what they did and

they had some previous ELISA data

on this sample set and, they had run

IL-6. And then a couple of weeks

later they came back to us saying that

we have no correlation for IL-6.

And we were like, even

at the time, we were like, but we know that

IL-6 is working. We

have done correlations, we know it's

working. So we were,

me and the data science person. Um,

at the time, we were doing some

really thorough

troubleshooting, trying to understand how

they mixed up their samples in

any way, or the sample

manifest. We tried so many different,

combinations and

we used like, algorithms to try to

find matching,

uh, data points with

patients. And we were

doing this for so long. Uh, but

yeah, the only

explanation we would have is that they

have done a mix up

somewhere.

and then but

yeah, we didn't manage to solve it at the

time. But then, one

or two years later, one of

the PI's and that sent

those samples contacted us and were like,

actually, we sent you the, the wrong sample

manifest. Or the biobank had sent you

the, the wrong sample manifest.

So once that was solved, the

correlation looked perfect again.

Uh, that's great, that's

nice. It only took one or two

years. It only took

one or two years.

They figured it

out, right, that it wasn't

the IL-6 signal

and they're able to use that

data. Wow. And

you said you had two more

stories. What's the last

one? Yeah, I have another one, which

was, from the same

region in the world. Actually. There

seems to be pretty grumpy over there.

That's really

funny.

No joking. We won't

be making a joke on that. It's

fine. Just

correlation. Just a correlation.

This is just anecdotal, it just

happened to happen. But anyway.

Uh,

this was like one or two years

later. And we had a customer,

who we had also like, convinced to

like, can you please try out Olink? We

think it would be great for you to try

it out.

So they put together some samples. They

were working on this, ah, rare disease.

So they didn't have that many samples,

but anyway, they sent it to us

and we ran it and we sent

results back. And they came

back and they were so

upset again because they didn't have

correlation this

time around. I think it was,

for MSD (MesoScale Discovery)

and OLink, um,

for our audience. To be clear,

MSD stands for

MesoScale, um, Discovery.

So MesoScale Discovery is another

multiplex, ah, sort of protein

analysis platform, say, data from a

different sort of orthogonal technology.

So go right ahead. Yeah.

Right. So we

were again investigating, have we

done anything wrong in the lab? What's

going on here? And

then we had, um, a

couple of meetings where we tried to

ask them, so, did you actually run the

exact same samples using

the both technologies? Did

you run the same sample

matrix? Um, and

first time around, they said,

yeah, we run the same sample matrix. We're

like, okay, so we did some further

investigations. Couldn't

really understand what's going on, so we

schedule another meeting. And

then I really asked, I was like, So

what samples did you send to MSD? Was it

serum or was it plasma? He was like, It was

serum. I was like, okay, but,

uh, what you sent to Olink was plasma,

right? And he was like,

yeah, but they say it doesn't matter.

I was like, no, it doesn't matter if

you like, for the technology,

but when you look at correlations,

there's different matrices.

Uh, and he was like,

oh, yeah, okay.

Right. So after that meeting,

he became our best friend

and our best advocate.

Yeah. You turn the

negative into the positive,

and that is

remarkable. And so what about a

year, year and a half ago, you made a

transition from technical support, right.

You became a Director of Technical support,

growing the team. Oh, by the way,

how you went started from the first

technical support person, and when you

moved positions, within Olink,

how large was the technical support

group?

so my group was

around ten

people at a time.

Um, when I started

tech support, I was doing tech support and

Field Application

scientist role in the same

role. And then, like, I

think in

roles so that I was doing only tech support,

and we had other people taking over the FAS

responsibilities. So we had I

mean, the tech support group was

about ten people, but then we had

the FAS team that were, I

guess, around 20 people at the

time. Wow. So you

went from a technical support

field application scientist role as one

person to now 30

people. That is

so cool. And then you were involved

then in the hiring of all these 30 people,

is that correct? Because you had yeah, not

all of them. I mean, I hired a couple of

them, and then they, uh, kept on hiring new

people. Yeah.

Ah, wow. Illustrating the growth,

right? Yeah, but it's been a great

experience. I mean, I've trained many of

our labs that are still running Olink.

Great. Yeah. That's nice. Katarina, you

mentioned about 96 and Target, and

I'm guessing that this is part of our,

let's say midplex. And

this, uh, is Midplex product. Would you

like to give a little bit of overview what

we have now in our portfolio, like,

for Midplex.

Yeah. So for Midplex, we have

our working

horses. The target

it seems. So we have 15

of them.

Um, where, um, we have 14 human and

one from mouse.

And then we have

a, lower plex panel. The Target 48

cytokine, which,

gives um, data

in ABS quant. So in picogram per

mL. And, with this

panel? We have put

a lot of effort in pulling

in the right proteins,

um, to this panel. And

it's been really appreciated on

the field because people

really the goal that we

had was like, get the best

targets into this panel. And what we hear

from customers is that you've actually

managed to put them in the same panel.

So if I run other technologies, I have to

run multiple kits or multiple

panels from them. But you have them in the

same panel. Um,

that's great. And now your

current role is product manager

for two other products.

Yes. Right. So now we're

getting to the heart of sort of your day to

day. Exactly.

Now

actually, starting from this year,

I'm product manager for Flex and Focus.

Whereas last year I had the whole Mid

Plex,

portfolio.

But since we have now launched

Flex, um, I'm going to

focus on these two

products which belong to our

customized offering.

Sure. So if you can first talk about Focus,

because a lot of people may not be aware

of Olink Focus.

Yes, Focus is our, like,

really premium

product. So

this is a product that we develop, like,

from scratch for our customers. So,

like, from having the antibody

and the oligo and putting them

together to produce the

probe,

um, and...

we work really closely with our

customers for whom we develop these

panels. Um, we

have you can

select different levels of

validation so you can

have the basic validation offering, but then

we have several other layers

that you can put on

to really tailor it to the needs

that you have. And

you can, select

assays from our whole

and put them in to your

smaller 21-plex...

panel. And we can also,

like, build a panel for if you're

interested in CSF or Urine

or like, supernatants. So,

yeah, we can tailor-make this panel for you.

Meaning you're actually adjusting the panel

for the type of sample the customer plans to

run. So if they wanted just to analyze

urine, that Focus

panel of 21 out of

remarkable, right, we have 3000

antigen / assay pairs. Right.

And then you then shrink it down to

any sample type, but you're saying, okay,

customer wants to test or

routinely examine urine and to

optimize it for urine. Is that correct? Yes,

correct. Just like that. That's

amazing. That's

amazing.

What you tell me then, uh,

about Olink Flex? This is our newest

offering, right? Yeah, it's our newest

offering. So, this is,

somewhere in between our

fixed panels and the Focus panel.

So what we've done for Flex

is that we wanted to

offer a quick way

for customers to pick and

choose, proteins that they're interested

in, and get them in a

kit, like, within weeks, instead

of this long validation

development work that we have with Focus.

Um, so what

we've done for Flex is that we

have, built a library

of approximately 200

proteins, with

high,

inflammation,

uh, content,

basically.

the way it works

is that we have prevalidated all of

these, assays in

house using the same

level of validation as we have for our

fixed panels.

Then we have

to give a

picture of it. We have

Olink, and, um,

then, our customer

says, well, I want these,

tubes, pull them into another tube, and send

it to the customer. That's great.

Ah, is it already available, Katarina?

This product? Perfect.

Yes. And where can

I see the list of 200

proteins?

So, ah, you can, of course, go

to Olink.com/flex

where you will

find the list of proteins and all the

validation data and the validation data

package,

describing exactly, how

we have validated the product.

But what we have also

developed for this product, which is,

I think, like, super cool

and very unique, is that we have

this

panel design builder

within our digital,

platform

Olink Insight. So

you, as a customer, can log into

Olink Insight, go to the Flex

section, and there you will have your

own, like, workstation,

for building customized panels.

So, two things,

right? Olink Insight can be found

at https://insight.olink.com

insight.olink.com

And I think the other observation is

you've used "Cool" Now twice in

this conversation. First,

when you joined Olink in

Really cool

company. So, is Olek still a

really cool kind of company.

Right? Things are happening

all the time. New things are happening.

We're like, we're really on the

front line of things. It's

exciting. We, have

Leading Edge, right? Yeah.

I just went, like, this panel designer and

Insight. We

have competitors, right, that have

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What is Proteomics in Proximity?