Proteomics in Proximity

A discussion of a recent Scientific Advances paper by Katz et al comparing aptamer- and antibody-based protein detection methods using genetics and clinical traits, just in time for the American Society for Human Genetics conference (#ASHG22) in Los Angeles.

Show Notes

The 2022 Science Advances paper, “Proteomic profiling platforms head to head: Leveraging genetics and clinical traits to compare aptamer- and antibody-based methods” by Daniel H Katz and Rob Gerszten et al. is available online here.

A highly informative Twitter thread by the first author Dr. Daniel Katz reviewing the figures of the paper is available here.

If you are interested in learning more about the use of proteomics in multiomic strategies, here’s link to the Olink website where examples of combinations of omics methods are combined.

If you would like to contact Dale, Cindy or Sarantis feel free to email us at info@olink.com.

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 Clamydas from Olink

Proteomics talk about the intersection of

proteomics with genomics for drug target

discovery to 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.

Welcome to the Proteomics and

Proximity podcast. This is your host,

Dale Yuzuki, along with

Cindy Lawley and

Sarantis Clamydas.

We want to welcome you today. We've got

some pretty exciting news,

and by news, I mean a recent paper.

And Cindy, would you like to introduce it?

Sure. So this is a, uh, paper that came

from Daniel Katz and Rob Gertszten and a

series, uh, of co authors as well.

It's in Science Advances, so

very, uh, prestigious

journal. And we've known

that the work had been done for

quite a while and we knew that it was going

through peer review. And so we've just been

very excited to see it come out and

we're excited to talk about it today.

Uh, go ahead.

Yeah, sure. It's a comparison paper among

proteomic profiling platforms.

It looks at antibody, uh,

methods versus aptamer

methods. The pros and cons

pluses and minuses. I think

that, uh, both platforms are

incredibly valuable. So the two

platforms that were compared to

the SomaLogic platform, their

also their 5K.

Uh, and the OLink platform, this was

our previous product, the Explorer

which uh, they called the Olink 1.5K

So yeah. Dale, do

you want to give us a little bit of

background on Soma?

Sure, happy to. I was

a protein product manager for QIAGEN back in

the early 2000's. And

this was maybe

Protein Science Conference and I

met a very interesting individual named

Larry Gold. He was the

founder of SomaLogic, a

company in Colorado. He's a

genius, really. And he says, yeah, he's a

genius. A uh, remarkable individual.

He made quite an impression on me because he

was working on a very unusual

approach to protein detection. Instead of

using, uh,

recombinant antigens and

inoculating

goat or mouse or rat to

develop antibodies right. Or develop

hybridomas or

monoclonals, he actually

was using a method of in vitro

selection.

it's in organisms like

microorganisms, a series of

selective processes,

to find out particular

synthetic nucleotides,

either RNA or DNA, that would

bind very specifically to

proteins. This is called aptamers. These

are synthetic stretches

of DNA and RNA

that bind to proteins, you might say. Well,

we have DNA binding proteins,

RNA binding proteins all the time. Well,

yeah, transcription factors, what have you.

But these are proteins that have been

evolved to bind to DNA sequence. Now

we're doing the reverse. We're actually

finding out what sequence can

bind to a particular protein

that doesn't normally bind to

DNA. Well, they've evolved,

the technology evolved,

uh, not only

to use natural

oligonucleotides, they actually have,

uh, developed something called Somamers,

which are unnatural

nucleotides, uh, because if

you think about the positive charges

of DNA, etc, etc, etc, etc. All

you're going to have certain limitations in

terms of how the DNA and RNA can

fold, using natural

nucleotides. So they've

developed some chemicals they call

Somamers to

expand the kind of,

uh, synthetic aptamers,

what it's called, to bind

to more and more proteins. So

they've scaled the technology in

earlier. They published some really high

profile papers. And what these papers

did was look

at 1300 different

proteins out of the circulation

and connect it to the genomics. So what

they would do is, frankly, since you

had a million genotypes from,

um, an Infinium array,

a genotyping array, and you have all these

genotypes from individuals, they can do

GWAS to protein level using

the SomaLogic, um,

readout. Again, they're doing

GWAS to the circulating protein

level and then connect that to

phenotypes. And the

first high-impact papers in Nature and

in Science were pretty remarkable

because you're talking about genomics

and you're tying it in to the

circulating proteome, which you then can

tie into disease. Yeah. And

I'll ask, I'll also add

is, you know prevailing

technologies and mass spectrometry. Of

course, looking at proteomics,

um, amazing advances there

and a lot of,

um, transition to the clinic, some of the

discoveries there. The idea of

being able to, um,

hook out of, um,

a plasma sample, for example,

uh, to hook with an

affinity-based method like a

Somamer allows uh, you to

do the low abundant proteins, the ones that

just may not show up very often or in very

high abundance in plasma

allows you to start to look

at patterns of

those proteins as well with

health and disease. And so I think this

is amazing innovation

is that in Mass Spec, of

course you can do this, but it takes

a lot more sample. And doing it in large

numbers of samples can be challenging simply

because of, uh, what it takes

to put a service wrapper around

running many samples through a mass

spec. Uh, I think you bring up a really good

point, Cindy, which is people just

say, well, why can't you just use mass

spec? It's a mature technology, it's

been around a long time. HDL, LDL,

we've got a lot of great clinical

labs from it. Exactly.

But it's the level of

abundance, right, in that there's a

number of really important

molecules that aren't

very prevalent in the

circulation that both, uh, the

Somamer technology and the Olink technology

can pick up that Mass Spec

simply cannot. You also bring up the other

point, which is Mass Spec,

uh, has a lot of, uh, sort of

upfront steps, right? If you're doing

liquid chromatography, tandem

mass spec, there's a whole bunch of

sequential... steps uh,

you have to do that is just not high

throughput is that correct? Yes. And

managing the

variability. Yeah. Sarantis? Sorry. Go

ahead, please. No, I'm sorry, I was talking

about Mass Spec and plasma. We know

that in plasma there

are like 40 or 50 proteins that are super

abundant. And when you try to run a

Mass Spec, you let's say mask

all the other proteins. And for this is for

uh, low abundant proteins. I think affinity,

capture assays,

like Olink assays, can

help to identify this, because

you overcome this problem with Mass Spec.

It's a nice complementary

approach, obviously. Yeah. Sarantis,

it would be great if people could get kind

of an overview of how Olink is different

from the SomaLogic approach, because of

the two platforms being compared in this

paper. Would you mind tackling

that? Yeah, I mean,

in this paper, actually, they have

uh, used Olink Explore

our first Explore based on the

NGS platform. And

SomaScan

That's the most expanded version, with more,

let's say, reagents. And they

profile Jackson Heart study,

around

500 people, and

Heritage Family Study

Uh, in the first case, when they

see, they try to see overlap in between.

SomaScan 1.3K and Olink they

see like roughly 500

proteins being overlapped. When uh,

they switch to the

expanded version of SomaScan, they were

able to see like

uh, actually

proteins. The nice advantage of Olink,

of course, is uh, NGS based

approach and uh, the

antibody capturing that gives

obviously more

specificity compared to

others.

But... yes, please. So if I

understand correctly, you mentioned the

Jackson Heart Study of what, some

Yeah, uh, 500

individuals, yes. And I have to say

here, for all of these

individuals, they have like

whole genome sequencing data.

Yeah. Mmm hmm, so really nice genetic

background information. As far

as a comparison study.

Right. Actually, uh, before we talk about

the study itself, Cindy, did you do some

research on Jackson Heart? Can you tell me a

little bit more about that? Yeah, so Jackson

Heart Study is a community-based, it's

a longitudinal cohort study. So uh,

essentially they're looking at

understanding, cardiovascular disease

primarily, but also renal respiratory

diseases. The nice thing. And what

I love about the Jackson Heart study is

that it's African Americans.

So, it's really

helping us get a view into

proteomic variability, not just

within uh, the northern European

populations that have been characterized so

well, genetically as well as now,

um, quite a few of them have done

proteomically, like the UK

Biobank. But um, it allows us to get

some sense of the diversity in

African Diaspora.

So... the

Jackson and Jackson Heart study, then, is

referring to Jackson, Mississippi? Is

that right? That's right, exactly.

The majority of people then are from

Mississippi area. That's right, yeah.

And when you say longitudinal, are you

meaning that what they're followed up

over time? Exactly.

So they were

recruited, and

then followed,

over time. The nice uh,

thing about that is as people

evolve, as they

get older and they have health

challenges, those health challenges

can be better understood by

looking back at samples

before they had diagnosis of

disease. And that's going to help

us develop more preventative

approaches... for

diseases. Today, our healthcare

system is a

diagnosis-based system,

not uh, only within the US.

But also worldwide. Really, the

funding for healthcare,

revolves

around diagnoses. And

so this concept or this ability, and

I think I would argue this

might be, um, one of Larry Gold's

big motivations in

developing uh, of the SomaLogic technology.

I think we're really excited about this for

Olink as well, is the ability

to be more preventative and

understand risk, not only

from the genetic perspective

that has been enabled over the last 20

years, but also from the proteomics

perspective. And so understanding proteomic

risk at any given moment,

um, appears to be

providing a little bit more of a window into

more real-time health. And so I think that's

the important aspect of having longitudinal

data like this, especially in this

underrepresented population. So this

is a pretty expensive undertaking because

we're talking about whole genome sequence

out of these 568

individuals. Right? And then you're also

talking about,

Olink 1.5,

Olink Explore 1536 plus,

SomaLogic 1.3K on all

the same samples. Do I understand

that correctly? That's right.

And as far as

then, what can you tell me about the

Heritage study? Yeah. So this is

health risk factors and

exercise training and genetics. So the

HERITAGE stands for

literally, that: HEalth RIsk factors.

exercise, Training And GEnetics.

And it's, ah,

um, a

partnership among I think it's seven

universities, actually. I don't have those

seven universities. Maybe it's five

universities. Sorry. Yeah, I think it's

five universities and I don't have them

off the top of my head, but they're across

the US. As well as Canada. So,

um, really trying

to get information

across a large segment of

the population and

again, whole genome sequencing

information. Is that right, Sarantis?

That there was whole genome sequencing

within the Heritage as well? Yeah,

which like you said,

Dale, it blows my mind because I think

about the exome

um, sequencing consortium from the UK

Biobank, which is a massive undertaking,

but still exome sequencing is only

about 1 to 3% of a whole genome

sequence. So we're talking a lot

of sequencing to your point. And this is

really important, especially for health

equity, because we really have an

underrepresentation of African

Diaspora in um,

in sequencing data and

you know being biased

by a chip that might not have a lot

of content that was

designed, a genotyping chip, I'm thinking

about the comparison between a genotyping

chip versus whole genome, um, sequencing.

It's sort of like getting a

satellite view of a

population census.

You can take a picture from the satellite

and you can estimate the representation

in those,

those buildings that are in an eight

block area. For example. Or you can go

house to house and you can knock on the

doors of every one of those

residences. Which one is going to give you a

more accurate representation of the

population? The knocking on the doors.

But... it's going to be a lot more

expensive as well. Right. So whole

genome sequencing goes base to base

to the extent that our sequencing methods

allow that. And we're going to see diversity

that we might not know about

before... when we developed that

genotyping chip initially. Yeah, that's such

an excellent point because you just assume

all, you just get the genotypes and then

you capture the majority of the

variation. But what you're saying is

yes, the whole exome sequencing doesn't

capture a fraction of

that variation because right. These SNPs

are in non coding regions.

That's right.

And to be able to get them the whole

genome sequence, we can get a very

fine grained look, right, at the

variation within the

population and then the connection to

risk and disease. Is that right? Yes. And

you don't know what you don't know. Right?

Uh, so the fact that this is whole

genome sequence, I just think

Rob's team did a

phenomenal job of bringing together

important data to really,

advance our understanding not only of

the two platforms, but also of

advancing health equity.

Yeah. Yeah. There was something

else. I'm sure it'll come up

again if I think of it, but

um,

it's essential

that we characterize diversity in these

populations. Sure.

So Sarantis, what can you tell me then about

the primary findings?

Actually, really uh,

nicely, they try to

see if there's a correlation with

cis-pQTLs. It's something that comes

again, we have seen it in other papers

as well, that Olink assays based on

antibodies has a nice correlation with

cis-pQTLs. And actually

Olink Panel is uh,

associated with new

pQTLs. And I think that is a

really important, um,

finding, especially if you want to identify

new biomarkers and drug targets. Right.

Do you agree? Yeah. And

we have seen a lot of cohort studies

actually, uh, Cindy you can have some from

your side and from your experience as well

on that. I was just going to define cis-

pQTLs again. I know we've talked about

it previously, uh,

on episodes of this

podcast, but as a reminder,

a cis-pQTL is a correlation

in genotypes at a locus,

with protein levels.

Again, just a correlation, but

um, it's something you can detect through

statistical analysis and large data sets.

And of course, the larger your data set, the more

your power to detect any

association. Right. So a cis-

pQTL is when a variant is correlated

with protein levels. If that variant is

within a million bases or

itself that is coding

for that protein, that's what we call a

cis-pQTL.

So it makes good logical sense, makes

us feel good about the measurement of

the protein. If you actually see

a correlation between a region that

is coding for the protein and that protein

itself. The...

other thing to say about that is there are

good biological reasons why sometimes you

might not see a cis-pQTL

there's protein-protein interactions that

might knock that protein

level off of a, uh, correlation

directly with that region.

But... it is a nice feel-good

measure that you're measuring the right

protein when you do see a cis-pQTL

association. And that's a tool that this

team used.

And to be absolutely clear, "cis"

means it's within that one

megabase... close. And

P... Yes. And "p" stands

for protein, and QTL

stands for "Quantitative Trait

Loci". So you're saying that

a particular SNP genotype,

the loci, is actually

controlling the level

of protein as a

quantitative trait. I'd say appears to be controlling it.

Appears to be controlling. Associated,

yeah. Right.

Statistically associated, yes. You can't

say causality at that point where you're

just looking at correlations. So we're

associating the presence of a SNP

within 500,000,

bases of a particular

gene, and that

SNP is positively

associated with the level of

that protein in terms of

Olink-quantitated or SomaLogic-

quantitated understanding.

Associated. Yes, that's right.

Okay. And the value of

these pQTLs

is?... So the cis-

pQTLs in this paper were used as a...

a surrogate measure of

specificity. But in

general, cis-

pQTLs as well as trans-pqtls. And those

are ones that are

correlation with protein level that's

outside of the gene that's coding for that

protein. And that means outside of

that one megabase region, uh,

around that gene,

that those are valuable because they

help us understand the

pathways that may be

important for diseases

that are associated with not

only the proteins, but also diseases that

we've identified in the past through

GWAS. This catalog of

amazing GWAS

associations, uh, it helps us understand

what protein pathways are

involved in those diseases. And then, of

course, if we have a sense of protein

pathways important in diseases, that

gives us the ability to start

to propose therapeutic

targets or ways that we may

develop therapies

to go after these proteins or

to go after the mRNAs that

are translating to

proteins. ...

To then have an

approach to either nudge people

back into health away from disease that's

that preventative side or

as what our health care system

pays for today. Which is drugs to

treat diseases once they've been

diagnosed. So here we get the

payoff of the Human Genome Project

right? Which is new

drugs, new diagnostics,

new therapies potential

for cures, is that correct?

Potential for cures. Right! Which

is right

now, what do we say, 90% of

clinical trials?

I think that's the latest number

that I've heard. 90% of

clinical trials are

failing. And the

ones that you have genetic information

going into the clinical trial, uh, have been

reported to be twice as successful,

so twice as likely to be

successful. So the question is, what

can proteomic signatures from

SomaLogic or Olink. What can these

approaches do to help

improve the success of clinical trials? I

think that's yet to be seen, but that's

certainly the hope

of the future.

And using leveraging large data sets like

these important studies like Jackson Heart

study and the Heritage Family study.

Uh,

Sarantis, you mentioned 40%

in terms of cis-pQTLs. Sort of

getting to that, right? What did you

mean by that? That means

that from the old thousand

five hundred proteins,

uh, that they check from Olink

platform, more than 40%, they

are correlated with new cis-

pQTLs. And I think that was really

amazing. That's a really amazing number,

because it gives the possibility to identify

new biomarkers, for example, as you

mentioned before, a new drug

targets. And,

and the nice thing of the

Olink assay is that not

only, uh, were

correlated proteins, they are having

cis-pQTLs, but also when they don't have

correlation with the Soma assay, we have,

cis-pQTLs. That means

they have a really nice

capability in the Olink

Explore to identify this

cis-pQTLs. That's the take home message

from this. And so what was the

percentage relative to

SomaLogic? I think Soma if I'm not

mistaken, it's like roughly

So the higher percentage than the

overall numbers, were they also

the overall numbers are

different? Yes,

of course.

I think I'm just looking at the paper

as you're talking, Sarantis. I think for Soma

there were 370

of 1301,

cis-pQTLs detected for

Olink, it was 575

of 1472 total

measurements, uh, where they

detected cis-pQTLs. But like I

said, there's good reasons why we

might not sometimes detect a cis-

pQTL. So I think one of the

interesting aspects that I didn't see

them, um, I saw a little

bit of this, but the ones that they have in

common between the two platforms, if

you can see a cis-pQTL on

one that would suggest that there should

be a detectability of a cis-

pQTL, then you should be seeing that

on the other. And in fact, I think the

comparison between the two I

think the median comparison was

about 41% between the two

platforms. Am I remembering that right? I

didn't bring that pull that figure out.

Um, and so it's

compelling, right, to

wonder, is one platform

actually pulling in a, um,

phosphorylated version of the protein,

as well as the protein, uh,

without the phosphorylation,

which may

be good information to have. If you

map those epitopes, then,

um, you can determine that, I

think. But, uh, I think that's the value of

being able to look at both

technologies together and the

complementarity of them. And I think David,

uh, does a nice job of

characterizing that. And I will also

point to something you showed me. I think it

was you, Serantis. You showed me the

tweet, um, that David put

up on Twitter that has a

beautiful walks us through

his primary findings which maybe we can put

a, ah, link to that Tweet in the show notes.

I see. Yeah. You're referring to Daniel

Katz, the first author. I know it's hard

to visualize no

worries. It's hard to visualize large

numbers. Right. We're talking about roughly,

what, 370 out of

Um, and so, yeah, these numbers,

they're hard to remember, but nonetheless,

I think the take home message,

right, is that when you compare

both side by side on these particular

platforms, sort of the

findings, uh, of cis-

pQTLs is really

important. It can be useful as a

discovery tool. The

overlap is what you're saying? The

overlap between the 370 and

pretty low, is what you're saying.

Yeah. Is that right? I think this

specificity analysis, uh, that

they did was, um,

super important. I think another aspect

where we weren't showing

up as,

um,

beneficial, as I would love for our

assay to show up, was in

precision, in what they assess as

precision and repeated measurements. So

Sarantis, you had a really good explanation

of that. Do you want to go over what they

talked about in the paper? There

actually, um, authors, they have seen

that Olink has bigger

CV's than, the Soma platform.

There could be a lot of reasons, but they

speculate that one explanation

could come from the fact that Olink, we are

using small sample volume for,

our assays. Another

explanation could be for the fact that

Olink antibodies are

polyclonals. This could

affect precision, but may also

make them more resistant, making them more

resistant to binding interference.

That means that it will capture some, uh,

complexes, some protein complexes, that

aptamers could not see or could not capture,

because their interface are

covered by proteins.

So one of the advantages of using

higher volumes in an assay, certainly I

think, might be that your

coefficient of variation are these

CV's, which are surrogate

measures. We've got these surrogate measures

of precision, repeated

measures being right spot-on

top of each other.

Um, that might be

a good reason to

have higher volumes. There are trade offs,

right.

They have seen also that if we pool

sample plasma, then we

improve our measured CVs. I have seen this happen.

One of the very strengths of

Olink, which is using minimal sample

volumes, what Explore only needs like

six microlitres is actually a

weakness, which is interesting

in terms of the variability. But I think

we can say that the pQTL

results - right - do speak for

themselves. But there's another angle in the

paper that I think is some of the

strongest data. And

this is regard to phenotypes,

right? Yeah. And it speaks to

just to touch back on the precision. So, in

a vacuum, or when only

thinking about precision alone, when you

have higher CVS or higher,

um, variability, you need

bigger sample sizes to

detect a difference between, say,

cases and controls. In a biomarker study,

for example, uh, and

so if you're just thinking

about precision in that way, it's really

important um, as a

consideration for power. And

so then, Dale, this uh, is

where the rubber hits the road, right - is trying

to make a phenotypic

association, in the real

world with disease.

Do you want to summarize

that for us

from the paper?

The phenotypic, uh,

results? Yeah, those

phenotypic results were really interesting

because they pulled out some

eight different phenotypes

from total cholesterol to

EGFR to body mass index.

And they show this bar chart where. The

Olink

pQTLs right, compared to the

SomaLogic pQTLs, there's

this huge difference on a

phenotype by phenotype

basis. And thinking about

it, it's well this is really what

you care about, which is phenotypic

associations

between the genetics and

the particular thing you're

measuring. If it's uh,

hemoglobin A1c, if it's

systolic blood pressure,

these are biomarkers, these are

phenotypes from the population that

they really care about. Why?

They even have an association with

ASCVD risk score. And if

you've taken a physical

recently, your doctor will actually

have you calculate your

ASCVD risk score. And I was

really surprised in my last physical where

I'm punching in the numbers and they're

saying, okay Dale, you've got an elevated

risk of 4% and we need to keep an eye

on this. Uh, but

nonetheless, those phenotypes are real

world, everyday rubber meets the

road, like you mentioned, Cindy.

Yeah, it's

exciting because this is really what Larry

Gold had in mind. I think this is

what Ulf Lungren had in mind

in terms of being able

to broaden uh, a

discovery platform for

proteomics.

Sorry,

I'm sorry. Go ahead, please go. Sarantis,

please. No as an example,

brings up the HSP-70, it's

really uh, well-known and

famous

heat-shock protein 70

and connected

correlated with a BMI. And there are a lot

of studies nowadays for,

drugs against uh, the activity of

this protein. Actually, that's uh,

really exciting finding.

And regarding heat-shock protein

a handful of Elisa's at the very end of

the paper. Sarantis do you want to

comment on that? Yeah, I

mean they try to see

which of the

Soma and Olink proteins correlate

better uh, with uh,

Eliza. Of course they use Eliza that they

are, let's say, commercial available for

this. They focus on these

five targets, let's

say. And overall it's really

striking how Olink

data, correlates really nicely

with Eliza

data. And uh, again,

they focus with HSP-70

and a handful of other proteins

that really nicely

correlate uh, the two assays.

Giving, again a

bonus to Olink for

specificity, I think you agree on that case.

Well, and I think, I think the ability to

translate to a clinical

tool and to be fair, Eliza

is immunoassay based, right? It's

immuno-absorbent based and

we're an immunoassay.

Olink uses two antibodies for

each protein, whereas Soma

has this novel aptamer

technology, this synthetic...

aptamer technology, uh, that

they've innovated. And so,

yeah, it's something

I like about antibody-based

is that so many of our, uh,

therapeutic targets that

we use

that have passed clinical trials are

antibody-based.

Well,

thank you both for really

excellent analysis of a side by side

comparison paper. For those

interested in the reference, this is

Katz DH. This is Daniel Katz,

the first author. The senior author

is Rob Gerszten. The

title is "Proteomic profiling platforms head

to head: leveraging

genetics and clinical traits to compare

after an antibody based methods" Yeah.

Thank you very much for joining us today.

Thank you very much. "Go Beth Deaconness!"

right? "Beth Israel Deaconness"

Exactly. Thank you very

much. Thank, uh, you thank you. Thank you.

Bye bye. Thank

you for listening to the Proteomics in

Proximity podcast brought to you by

OLink Proteomics. To contact the

host or for further information,

simply email

info@olink.com.