January 28, 2025 • 32 mins
Welcome to the Olink® Proteomics in Proximity podcast!
Below are some useful resources mentioned in this episode:
Olink® Reveal, accessible NGS-based proteomics for every lab: https://olink.com/products/olink-reveal
Olink tools and software
· Olink® Explore 3072, the platform utilized by the UK Biobank to measure ~3000 proteins in plasma: https://olink.com/products-services/explore/
· Olink® Explore HT, Olink’s most advanced solution for high-throughput biomarker discovery, measuring 5400+ proteins simultaneously with a streamlined workflow and industry-leading specificity: https://olink.com/products-services/exploreht/
UK Biobank Pharma Proteomics Project (UKB-PPP), one of the world’s largest scientific studies of blood protein biomarkers conducted to date, https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/news/uk-biobank-launches-one-of-the-largest-scientific-studies
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:05):
Welcometo the Proteomics in Proximity podcast,
where your co-host Cindy Lawleyand Sarantis Chlamydas from Olink
Proteomics talk about the intersection ofproteomics with genomics for drug target
discovery, the application of proteomicsto reveal disease biomarkers,
and current trends in using proteomicsto unlock biological mechanisms.
(00:26):
Here we have your host,Cindy and Sarantis.
Welcome, everybody.
I'm back from holidaysand it's my first episode for 2025.
Happy to see you all again.
Happy to see Cindy.
And I'm really excited to discusswith Jenny and discuss about proteins and,
yeah,looking forward to hear from you, Jenny.
(00:46):
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 questionabout how to pronounce
your first name, so I'd love itif 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.
(01:08):
And today we want to talk a little bitabout what's coming up.
In the future, we have a this recentlaunch that we'd love you to characterize.
And then we'll talk a little bit
about some of the meetingswhere we'll be attending.
Please take it away.
Thank you so much for having me.
I'm really excited to be in this podcastwith you guys.
(01:30):
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 scienceand product development.
(01:52):
So I did start my career in massspec proteomics as a researcher.
And after that I refocused to support,
by biopharmaceutical researchand manufacturing.
And my main function has so farbeen within product management,
which in essence meansdeveloping new products and ensuring
(02:13):
that the existing products that we haveare meeting our customer expectations.
And you're so good at leading a teamthat listens to customers.
So I just want to acknowledgeand appreciate you for that.
It's such a pleasure to be hereand have a have a product management
function that really, really listens.
And I would say,thank you, Cindy, but I would really say,
(02:35):
you know, I joined Olink, what can it belike three years ago or something.
And their focus on innovation
and advancing proteomicsis very special and very unique.
It was
for me, it was a match made in heavenbecause I could combine having
(02:57):
great products out in the market
that contributes to cutting edge science.
But the culture of innovation at Olink
has been there since startand no credit to me there.
But it's really nice to be ableto continue that culture of innovation.
You picked us and we picked you.
It's a match made in heaven.
(03:18):
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 proteomicsand, as CMS or mass spec.
(03:42):
And at the time, it wasa 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 saylike 30 proteins or something
out from the mass spec.
And not only that, but, you know,
the way we identified the proteinscould be based on one peptide.
(04:05):
And that peptide.
I had sequenced myselfin the mass spectrum.
So there were very limitedamount of digital tools to support that.
Not much like intelligence softwareor anything like that.
So that's where I sort of started.
That's where I,
and then I left that for itfor quite some years, actually, to go
(04:26):
to this more biopharmaceutical, industryand then came back to Olink.
And it was, you know, it's
groundbreakinghow much things have happened since then.
So, the fact that you can studythousands of proteins,
the connection that we automaticallyalmost have to genomics.
(04:48):
It's definitely a new era.
So, I would say it's
a huge
thing that kind of happened in proteomicssince 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 atproteomics from the genomics side as well.
(05:10):
From our first RNA seq experiment. Right.
So those first sequencersthat Illumina made, the 1G,
they were going out the door for folksthat were doing digital gene expression
at the time, had been using gene arrays,gene expression arrays
and were keen to to understand the linksto real time biology.
(05:30):
And now, as part of ThermoFisher Scientific, we have both the mass
spec size and some amazing innovationsin 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
(05:50):
that makes a difference for proteomicsto be more democratic?
Is it the NGS itself?
Or the NGS plus other protocolsthat would be integrated.
What is your feeling there?
Well, you know, if we're talking as wellabout mass spec here now,
I mean it's not our a core area,but it's definitely our college area.
And you know, within masswithin proteomics, mass spec is still a
(06:14):
gold standard but also here and remember,this is not my area of expertise anymore.
But there's a huge amount of thingsthat have happened here.
And I would say mainly or a lot of things,
of course, on the technology sideto make sure that we actually can,
have a much greater proteome coverage,obviously.
But also, the digital tools, I should say,
(06:38):
how do you understand the results,
how did you qualityassess 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 yearsand you come back to see that
both in within Olink, obviouslywho is really spearheading this market.
(06:59):
But also, what I have seen from
the outside has happened in the mass specarea 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.
(07:20):
So that transition to be able to lookat more proteins across the proteome,
particularly in plasma CSF,some of these liquid media were
mass spec may not be able to see those lowabundant proteins.
I think that that was gamechanging for me,
and that attracted me to this teamand this technology.
Jenny, you just had an announcement fromyour team about this new reveal products.
(07:42):
Can you tell us a little bit about wherethat fits in to the democratization?
Yes. And so yeah, and thank you for
for highlighting the democratizationbecause that's one thing.
So proteomics has been you know, it's
not foreveryone or hasn't been for everyone yet.
One of our overarching goals,
(08:04):
within product development is to make sure
or to enable our customersto utilize proteomics,
and make it accessibleto a broader research community.
And some of these accessibility
challenges that we're trying to address,
have been noted elsewheremany, many times.
One of them being cost.
(08:26):
So, to be able to run largeproteomics 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
(08:48):
and to get more support in understandingand trusting the data.
So those are the three things that, youknow, 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,
(09:08):
in the last years.
But I really want to mention somethingthat really made a big difference.
Is of course, the,
you can be first project,
where the data has been publicnow for over a year.
And we have so many,
new publicationscoming out from that project already.
(09:29):
So, and already now it's, you know,and that is sort of a game changer
within proteomicsI really just want to highlight that.
Well, and those publicationsare highlighting which diseases folks
can dig into.
So just for context, the UKBiobank Pharma Proteomics project
a few years ago, 13 pharma partnersagreed to run Olink as the technology
(09:50):
of choice against almost 60,000 samplesin the UK Biobank.
Now, there has been
publications around,
the 54,000 samples
that have been part of the flagship paperthat 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.
(10:13):
That flagshippaper has been cited over 300 times.
And so that certainly builds,
more of a comfortwith 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 preanalytical variation, and
I think you allude to thisin your complexity comment.
(10:36):
Jenny, you mentioned right or wrong,
they're perceived as complexand I certainly think that's true.
From the genetics point of view.
And I knowSarantis has a history in this as well.
Actually that was also my question, I'mguessing that the daily life
is not only happinessin product management.
You have a lot of challengesto go through.
And you mentioned the cost.
(10:57):
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 oneimportant aspect,
especially as a supplier,to really make sure that we can guide,
(11:17):
our customers in understanding their data.
So maybe we can go through thata 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.
(11:38):
You have to really take carewhen you do the sample collection
and how you handle the samples,
and really ensure that, you know, you haveyou can assess the data quality
at each stage to build that confidence.
So one of the things that we're doingis to develop tools to help our customers,
help our researchers understandif they have pre analytical variation
(12:01):
and then guide them through, whatthat 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 ownsamples, your own results.
And so it's really critical,
(12:21):
within proteomics
to really take that into consideration.
It's a great point.
And I think at the end, our main goalis the precision medicine right at the end
is like, having high quality datawhere we can enable precision medicine.
I'm sure Cindy,you have a lot to share, about this field
where you are really lookingclosely recently on that even more,
(12:44):
you know, and happyto hear your thoughts about
how do you see this precision medicinebeing enabled by proteomics?
And where do you seethese going, proteomics in this respect.
Yeah, absolutely.
So I think we're bettercharacterizing disease risk
in individuals because we're capturingreal time information.
And so the comparisons of polygenicrisk score to protein risk scores
(13:05):
have been really helpful in that regard.
There's some papers out of ClaudiaLangenberg's lab, as well as
Ben Sun, who's one of the one of the jointsteering
committee members in the UKBiobank Pharma Proteomics project.
He's published with the team
at BioXcellerate and Optima Partnersaround protein risk scores.
I think that's going to help usin understanding
(13:27):
how to better recruit for clinical trialsso that we can have clinical trials
that are smallerbut powerful sufficiently powerful
to see success in candidates.
And of course the abilityto have more successful clinical trials.
What do we say the candidates,
you know, 90% of candidates failwhen they hit clinical trials.
(13:47):
If we can improve clinical trialsby just 10%, we'll be the best
drug makers in history.
And then I would say thatchanges everything downstream,
because now we're really dialingin the right treatment for the right
patient at the right time, which ChrisWhelan talks quite a bit about.
And he's in our just recent episodeof the podcast talking about just that
and how proteomics is enabling thisand so ultimately those are the pillars
(14:12):
I see being moved, the pillarsof ultimately precision medicine,
which interact with clinical trialsand risk stratification.
And then each of those interactwith each other.
And I see all of those moving
upon the foundation
of an understanding of howgenetics, proteomics and outcome data
(14:32):
are associated and linked.
Thanks.
Yeah, thanks for asking that.
And, you know,
there was there's been a lot of discussion
from our customersand the research community regarding
how can we, understanddifferent technologies in this area.
(14:53):
How can we understandhow they complement each other
and can you help us?
Sort of guide us
how we trust the dataor how we analyze the data.
So I think that's going to beand that's normal
because proteomics is maturing in itself.
So I think
(15:15):
that would lead us backto a little bit on the mass
spec side where the complementaritybetween, 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 maybetake more of the plasma side.
(15:36):
So I think those kind of things,and then again,
as suppliers and enablersto 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 canand we will also guide you
(16:00):
in terms of understandinghow that performs
versus other technologiesand how they complement each other.
And I think that's going to be somethingthat, as we are maturing,
we're going
to see moreof 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.
(16:22):
Mike.
And also, I wanted to ask you Cindy,for sure you have the overall
this trend of suddenlya lot of people, due to the fact
that we have a lot of technologiesand other technologies.
Now we're talking about precisionmedicine, right?
And there are a lot of eventshappening around this, especially in ways
that they didn't used to have before.
What is your feelingand what is your feedback on that?
Because you are more in the field and,
(16:42):
you know, in discussionwith a lot of people.
How do you see this moving forward?
So I think these two topicsare very intimately linked.
You know, your referenceto our activities in the field
and working with customersand showing up at conferences
and our messaging Sarantis, and Jenny,your comments about having
a responsibility in funneling data
(17:04):
that are as accurate as possible into,
the algorithms for machinelearning and artificial intelligence
that will change our understandingof 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.
(17:25):
And as we get there,we have this responsibility to only put
the most specific, well-characterized datainto, those algorithms.
And I think that'swhere we on the side of caution.
I think that's why we have ostensiblyfewer proteins in our assay,
because we're very careful aboutgetting those assays into our, products.
(17:46):
And I think in many ways,that's your team, maybe not your team
before you joined, but you certainly havesupported and resonated for that.
And I think customers appreciate that.
And just knowing thatif we're detecting something, especially
if it's a intracellular proteinor a membrane bound protein,
if we're detecting it in plasma,where it shouldn't be,
that has the potentialto be an enormous opportunity
(18:09):
for discovery, by customersthat are seeing it there.
And so our detectionor our lack of detection
should reflect, I think, true biology.
And I think that's our messaging Sarantisat meetings.
Yeah.
So JP Morgan, we just had JP MorganI think the messaging at JP Morgan
or the take homes that I heard there were,essentially that these companies
(18:33):
are in many of the pharmacompanies are presenting,
they're moving into a growth phase.
I think we've had two years of challengesand funding and,
and pullback and contractionand, and caution
and I think there'sthis this bullish opportunity with Suisse,
some uncertaintyaround the political climate
and the change in leadershiphere in the US.
(18:54):
But some optimism.
And it just felt very buoyant there.
And then we have right around the cornerthe Precision
Medicine World Conference,which is founded by Tal Bahar.
And they're really buildingon what that momentum,
felt like at
JPMorgan or around opportunity and Visionin order to take action.
(19:18):
And so to really foster, an environment
of partnership,in this precision medicine space.
So I think that's, that's very exciting.
And Jennywill be talking a lot about reveal.
Can you just give us like a high leveloverview is where does reveal fit
into our product portfolioand where can people learn more about it.
(19:39):
Thank you, Cindy.
So again we talked about accessibilitybeing 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.
(20:00):
OlinkReveal is the little sister of Explore HT.
It's an inflammation oriented panel,so curated,
the assays are curatedbased on cis-pQTL associations,
with a strong connection to UKB.
A very strong, inflammation focus.
As I said, it's a thousand plex panel.
(20:22):
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
(20:45):
per sample.
So the cost is actually less than $100per sample. Wow.
Which means that it would be much easierto 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
(21:05):
as one tool in all the big populationhealth 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 Revealto make it super simple.
(21:26):
So you should be ableto just go in the lab
if you have a NGS sequencerand set it up and run it.
To get results really quickly.
So you can even run it manuallyor with a simple automation solution.
So no big investments to start up,but something that any genomics lab
already has.
(21:47):
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 exampleof how we democratize protein.
So that's great.
Yes it is, it is.
We've been waiting for this.
This is very exciting. Congratulations.
(22:08):
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 projectthat has been ongoing for quite some time
at R&D and we're super proud of this.
And really required a lot of dataanalysis of the data that are out there
(22:30):
that are publicly availablewhere we're allowed to go in and play with
and see what are the ones that havethe highest disease associations,
what seem most promisingfor having future disease associations,
where these cohorts just haven't been ableto afford to get into proteomics.
So I think this will offerquick publications.
And I think tracking the publicationsin review will be an exciting,
(22:53):
time to see labs
doing proteomicsthat have never even ventured in and then,
of course, the opportunity to validateorthogonally with mass spec
I think will be, also amazing.
That's a great point.
And I
think the choice of inflammationis really crucial
because inflammation, as all of usknow, is connected to our disease almost.
(23:16):
And that offers a possibilityfrom different types of researchers
for different diseaseareas to explore proteomics finally.
That's a great tool. Yeah.
I mean even in Alzheimer's disease, right.
Where there are clearly endotypesand some of them are associated
with information and some of them are not.
Being able to stratify
those patients in advanceof clinical trials, for example, might be
(23:38):
some application.
I know that several pharma have reportedthat, 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 proteinsusing a next generation sequencer.
And folks are seeing stratificationof these,
(24:00):
of these disease areasafter the clinical trial.
And they're seeing that these differentendo 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.
(24:20):
That would be really a great toolfor following protein biomarkers
really closely.
And I have a questionactually for both of you.
I think we’re discussing about now.
We discuss about tools that we’redeveloping now
with a perspective in the futureBut how do you see the future?
What do you see the challenges
and the wins we may havefrom the proteomics lab in the future?
I think Jenny first.
(24:42):
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 goingto see much more,
(25:05):
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.
(25:26):
So it's going to beI think we're going to see,
you know, also just on the first UKBstudy, we've already seen that happening.
That you're identifying these
really nice protein signatureswith a very strong
predictive power early on to say,you know,
if this patient will actually
(25:47):
get a certain diseaseseveral years in advance.
So I think it's going to be like,you know, from this discovery
to this more, clinical applicationsthat's going to happen quickly now.
We already had talked abouthow much has happened in a few years time.
Right.
So and just looking forwardand then in five years,
(26:08):
I can't even imagine,you know, what we're going to do.
but I think it's going to go like, youknow, it's going to be more multiomics.
It's going to be much more supportfor clinical proteomics.
And of course, we as a supplier,have a responsibility
to help that to happen.
Great, great.
Cindy, what do you think?
(26:29):
What is the future?
Well, I'll piggyback on somethingJenny said.
So thethe idea of being able to predict disease
many years in advance of getting disease.
I mean, that was really hot newswhen Keren Papier
and Ruth Travis and Karl Smith-Byrneand Josh Atkins, their paper came out.
There's, you know, seven plus years,it was a median of 12 years, 12 years
(26:51):
in many cancersof being able to predict disease.
And of course, those are those are many ofthose 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 getat a little bit of the detail there.
(27:13):
And I just sawthey had a preprint on prostate
cancer that characterizesome of the pathways in the immune system
that are predictive of a likelihoodof getting prostate cancer.
So I can't wait for thatto come out in publication.
So that brings up, the recent
announcementaround us running the entire UK Biobank.
That's 600,000 samples.
(27:34):
That's 500,000 individual with 100,000repeat samples at a 15 year mark.
Is my understanding,
being able to see that across
all of the diseases that are representedwithin 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 100,000that are followed up at 15 years.
(27:56):
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 around8,000
African diaspora samples, have around8,000 South Asian samples.
These are diverse, sets of samplesthat, as Rory Collins
(28:19):
says, aren't enough diversityfor us to really characterize everything.
But across the entire UKBiobank, we do, you know, effectively
have longitudinal representation,because if you get enough samples, you get
folksthat are in different stages of disease.
So though it isn't longitudinalin an individual, it can be, you know,
by being cross-sectionaland large enough in size can represent
(28:39):
some of the longitudinal aspects of
disease progression.
So that'swhat I'm really looking forward to.
And I expect those datato be published around 2027.
Which means that that the worldwill have access to
that international resource,
which is some of theand we talk about it as the UK Biobank,
(29:01):
but it is the internationally access to UKBiobank.
So it's an exciting time.
And just to add to thatthe statistical power of 600,000 samples,
would, you know, imaginewhat that would mean for understanding
rare diseases, for example,which hasn't been possible, really.
I mean, I know, we had that you could seethat also in the, in the sort of smaller
(29:25):
UKB, set from before,but with very few samples.
So I think that is also somethingthat is extremely important.
Already with these, the first papersthat we have or the small sample size
that you mentioned, Jenny.
Yeah, it's a small one.
I mean it we were able to seethere are great papers
(29:47):
from Claudia Langenberg, that we hadimprovement on diagnosis of disease.
Even better than clinical outcomessometimes.
And that was really impressive.
Well, it was really amazingfor 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 representationof those diseases. Exactly.
(30:09):
And ability to
you know, propose these protein scoresthat that
that will improve any over anythinga 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.
(30:29):
So with that, I will wrap up this episodeof, 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 trackthree and track four in Hall C,
(30:51):
and very closeto 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 folksthat are there.
Would be great.That would be a great event.
Really? Yeah. I wish you were here.
(31:12):
I wish I was there, but I’mlooking forward to hear your feedbacks.
I’m sure you have great feedbackfrom that.
Yeah, we could do an episode live.
Yeah,that would be great idea. Great idea.
All right.
Thankyou 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 Proteomicsin Proximity.
(31:35):
Huge thanks to our guests and authorsof such impactful publications.
I also want to thank you for tuning in.
Really appreciate you being here.
If you enjoyed the content of this
episode, please think about sharing itwith friends or colleagues
you think might be interestedin the content.
In addition, if you'd be willingto head over to Apple or Spotify
(31:56):
or wherever you digest your podcastsand give us a rating and review,
this will help others find the podcast
when they're searching for proteomicsor precision medicine podcasts.
And mostly I want to saywe would love to hear from you.
So we have a dedicated email address.
pip@olink.com please reach out.
Let us know what you're interestedin hearing, about what you care about
(32:18):
and any feedback on the episodesthat we have already done so far.
This is all about you,and so we're really keen
to make sure that we're meetingwhat you'd like to hear about.
Thank you so much, and we'll see you soon.
Welcometo the Proteomics in Proximity podcast,
where your co-host Cindy Lawleyand Sarantis Chlamydas from Olink
Proteomics talk about the intersection ofproteomics with genomics for drug target
discovery, the application of proteomicsto reveal disease biomarkers,
and current trends in using proteomicsto unlock biological mechanisms.
(00:26):
Here we have your host,Cindy and Sarantis.
Welcome, everybody.
I'm back from holidaysand it's my first episode for 2025.
Happy to see you all again.
Happy to see Cindy.
And I'm really excited to discusswith Jenny and discuss about proteins and,
yeah,looking forward to hear from you, Jenny.
(00:46):
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 questionabout how to pronounce
your first name, so I'd love itif 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.
(01:08):
And today we want to talk a little bitabout what's coming up.
In the future, we have a this recentlaunch that we'd love you to characterize.
And then we'll talk a little bit
about some of the meetingswhere we'll be attending.
Please take it away.
Thank you so much for having me.
I'm really excited to be in this podcastwith you guys.
(01:30):
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 scienceand product development.
(01:52):
So I did start my career in massspec proteomics as a researcher.
And after that I refocused to support,
by biopharmaceutical researchand manufacturing.
And my main function has so farbeen within product management,
which in essence meansdeveloping new products and ensuring
(02:13):
that the existing products that we haveare meeting our customer expectations.
And you're so good at leading a teamthat listens to customers.
So I just want to acknowledgeand appreciate you for that.
It's such a pleasure to be hereand have a have a product management
function that really, really listens.
And I would say,thank you, Cindy, but I would really say,
(02:35):
you know, I joined Olink, what can it belike three years ago or something.
And their focus on innovation
and advancing proteomicsis very special and very unique.
It was
for me, it was a match made in heavenbecause I could combine having
(02:57):
great products out in the market
that contributes to cutting edge science.
But the culture of innovation at Olink
has been there since startand no credit to me there.
But it's really nice to be ableto continue that culture of innovation.
You picked us and we picked you.
It's a match made in heaven.
(03:18):
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 proteomicsand, as CMS or mass spec.
(03:42):
And at the time, it wasa 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 saylike 30 proteins or something
out from the mass spec.
And not only that, but, you know,
the way we identified the proteinscould be based on one peptide.
(04:05):
And that peptide.
I had sequenced myselfin the mass spectrum.
So there were very limitedamount of digital tools to support that.
Not much like intelligence softwareor anything like that.
So that's where I sort of started.
That's where I,
and then I left that for itfor quite some years, actually, to go
(04:26):
to this more biopharmaceutical, industryand then came back to Olink.
And it was, you know, it's
groundbreakinghow much things have happened since then.
So, the fact that you can studythousands of proteins,
the connection that we automaticallyalmost have to genomics.
(04:48):
It's definitely a new era.
So, I would say it's
a huge
thing that kind of happened in proteomicssince 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 atproteomics from the genomics side as well.
(05:10):
From our first RNA seq experiment. Right.
So those first sequencersthat Illumina made, the 1G,
they were going out the door for folksthat were doing digital gene expression
at the time, had been using gene arrays,gene expression arrays
and were keen to to understand the linksto real time biology.
(05:30):
And now, as part of ThermoFisher Scientific, we have both the mass
spec size and some amazing innovationsin 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
(05:50):
that makes a difference for proteomicsto be more democratic?
Is it the NGS itself?
Or the NGS plus other protocolsthat would be integrated.
What is your feeling there?
Well, you know, if we're talking as wellabout mass spec here now,
I mean it's not our a core area,but it's definitely our college area.
And you know, within masswithin proteomics, mass spec is still a
(06:14):
gold standard but also here and remember,this is not my area of expertise anymore.
But there's a huge amount of thingsthat have happened here.
And I would say mainly or a lot of things,
of course, on the technology sideto make sure that we actually can,
have a much greater proteome coverage,obviously.
But also, the digital tools, I should say,
(06:38):
how do you understand the results,
how did you qualityassess 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 yearsand you come back to see that
both in within Olink, obviouslywho is really spearheading this market.
(06:59):
But also, what I have seen from
the outside has happened in the mass specarea 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.
(07:20):
So that transition to be able to lookat more proteins across the proteome,
particularly in plasma CSF,some of these liquid media were
mass spec may not be able to see those lowabundant proteins.
I think that that was gamechanging for me,
and that attracted me to this teamand this technology.
Jenny, you just had an announcement fromyour team about this new reveal products.
(07:42):
Can you tell us a little bit about wherethat fits in to the democratization?
Yes. And so yeah, and thank you for
for highlighting the democratizationbecause that's one thing.
So proteomics has been you know, it's
not foreveryone or hasn't been for everyone yet.
One of our overarching goals,
(08:04):
within product development is to make sure
or to enable our customersto utilize proteomics,
and make it accessibleto a broader research community.
And some of these accessibility
challenges that we're trying to address,
have been noted elsewheremany, many times.
One of them being cost.
(08:26):
So, to be able to run largeproteomics 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
(08:48):
and to get more support in understandingand trusting the data.
So those are the three things that, youknow, 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,
(09:08):
in the last years.
But I really want to mention somethingthat really made a big difference.
Is of course, the,
you can be first project,
where the data has been publicnow for over a year.
And we have so many,
new publicationscoming out from that project already.
(09:29):
So, and already now it's, you know,and that is sort of a game changer
within proteomicsI really just want to highlight that.
Well, and those publicationsare highlighting which diseases folks
can dig into.
So just for context, the UKBiobank Pharma Proteomics project
a few years ago, 13 pharma partnersagreed to run Olink as the technology
(09:50):
of choice against almost 60,000 samplesin the UK Biobank.
Now, there has been
publications around,
the 54,000 samples
that have been part of the flagship paperthat 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.
(10:13):
That flagshippaper has been cited over 300 times.
And so that certainly builds,
more of a comfortwith 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 preanalytical variation, and
I think you allude to thisin your complexity comment.
(10:36):
Jenny, you mentioned right or wrong,
they're perceived as complexand I certainly think that's true.
From the genetics point of view.
And I knowSarantis has a history in this as well.
Actually that was also my question, I'mguessing that the daily life
is not only happinessin product management.
You have a lot of challengesto go through.
And you mentioned the cost.
(10:57):
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 oneimportant aspect,
especially as a supplier,to really make sure that we can guide,
(11:17):
our customers in understanding their data.
So maybe we can go through thata 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.
(11:38):
You have to really take carewhen you do the sample collection
and how you handle the samples,
and really ensure that, you know, you haveyou can assess the data quality
at each stage to build that confidence.
So one of the things that we're doingis to develop tools to help our customers,
help our researchers understandif they have pre analytical variation
(12:01):
and then guide them through, whatthat 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 ownsamples, your own results.
And so it's really critical,
(12:21):
within proteomics
to really take that into consideration.
It's a great point.
And I think at the end, our main goalis the precision medicine right at the end
is like, having high quality datawhere we can enable precision medicine.
I'm sure Cindy,you have a lot to share, about this field
where you are really lookingclosely recently on that even more,
(12:44):
you know, and happyto hear your thoughts about
how do you see this precision medicinebeing enabled by proteomics?
And where do you seethese going, proteomics in this respect.
Yeah, absolutely.
So I think we're bettercharacterizing disease risk
in individuals because we're capturingreal time information.
And so the comparisons of polygenicrisk score to protein risk scores
(13:05):
have been really helpful in that regard.
There's some papers out of ClaudiaLangenberg's lab, as well as
Ben Sun, who's one of the one of the jointsteering
committee members in the UKBiobank Pharma Proteomics project.
He's published with the team
at BioXcellerate and Optima Partnersaround protein risk scores.
I think that's going to help usin understanding
(13:27):
how to better recruit for clinical trialsso that we can have clinical trials
that are smallerbut powerful sufficiently powerful
to see success in candidates.
And of course the abilityto have more successful clinical trials.
What do we say the candidates,
you know, 90% of candidates failwhen they hit clinical trials.
(13:47):
If we can improve clinical trialsby just 10%, we'll be the best
drug makers in history.
And then I would say thatchanges everything downstream,
because now we're really dialingin the right treatment for the right
patient at the right time, which ChrisWhelan talks quite a bit about.
And he's in our just recent episodeof the podcast talking about just that
and how proteomics is enabling thisand so ultimately those are the pillars
(14:12):
I see being moved, the pillarsof ultimately precision medicine,
which interact with clinical trialsand risk stratification.
And then each of those interactwith each other.
And I see all of those moving
upon the foundation
of an understanding of howgenetics, proteomics and outcome data
(14:32):
are associated and linked.
Thanks.
Yeah, thanks for asking that.
And, you know,
there was there's been a lot of discussion
from our customersand the research community regarding
how can we, understanddifferent technologies in this area.
(14:53):
How can we understandhow they complement each other
and can you help us?
Sort of guide us
how we trust the dataor how we analyze the data.
So I think that's going to beand that's normal
because proteomics is maturing in itself.
So I think
(15:15):
that would lead us backto a little bit on the mass
spec side where the complementaritybetween, 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 maybetake more of the plasma side.
(15:36):
So I think those kind of things,and then again,
as suppliers and enablersto 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 canand we will also guide you
(16:00):
in terms of understandinghow that performs
versus other technologiesand how they complement each other.
And I think that's going to be somethingthat, as we are maturing,
we're going
to see moreof 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.
(16:22):
Mike.
And also, I wanted to ask you Cindy,for sure you have the overall
this trend of suddenlya lot of people, due to the fact
that we have a lot of technologiesand other technologies.
Now we're talking about precisionmedicine, right?
And there are a lot of eventshappening around this, especially in ways
that they didn't used to have before.
What is your feelingand what is your feedback on that?
Because you are more in the field and,
(16:42):
you know, in discussionwith a lot of people.
How do you see this moving forward?
So I think these two topicsare very intimately linked.
You know, your referenceto our activities in the field
and working with customersand showing up at conferences
and our messaging Sarantis, and Jenny,your comments about having
a responsibility in funneling data
(17:04):
that are as accurate as possible into,
the algorithms for machinelearning and artificial intelligence
that will change our understandingof 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.
(17:25):
And as we get there,we have this responsibility to only put
the most specific, well-characterized datainto, those algorithms.
And I think that'swhere we on the side of caution.
I think that's why we have ostensiblyfewer proteins in our assay,
because we're very careful aboutgetting those assays into our, products.
(17:46):
And I think in many ways,that's your team, maybe not your team
before you joined, but you certainly havesupported and resonated for that.
And I think customers appreciate that.
And just knowing thatif we're detecting something, especially
if it's a intracellular proteinor a membrane bound protein,
if we're detecting it in plasma,where it shouldn't be,
that has the potentialto be an enormous opportunity
(18:09):
for discovery, by customersthat are seeing it there.
And so our detectionor our lack of detection
should reflect, I think, true biology.
And I think that's our messaging Sarantisat meetings.
Yeah.
So JP Morgan, we just had JP MorganI think the messaging at JP Morgan
or the take homes that I heard there were,essentially that these companies
(18:33):
are in many of the pharmacompanies are presenting,
they're moving into a growth phase.
I think we've had two years of challengesand funding and,
and pullback and contractionand, and caution
and I think there'sthis this bullish opportunity with Suisse,
some uncertaintyaround the political climate
and the change in leadershiphere in the US.
(18:54):
But some optimism.
And it just felt very buoyant there.
And then we have right around the cornerthe Precision
Medicine World Conference,which is founded by Tal Bahar.
And they're really buildingon what that momentum,
felt like at
JPMorgan or around opportunity and Visionin order to take action.
(19:18):
And so to really foster, an environment
of partnership,in this precision medicine space.
So I think that's, that's very exciting.
And Jennywill be talking a lot about reveal.
Can you just give us like a high leveloverview is where does reveal fit
into our product portfolioand where can people learn more about it.
(19:39):
Thank you, Cindy.
So again we talked about accessibilitybeing 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.
(20:00):
OlinkReveal is the little sister of Explore HT.
It's an inflammation oriented panel,so curated,
the assays are curatedbased on cis-pQTL associations,
with a strong connection to UKB.
A very strong, inflammation focus.
As I said, it's a thousand plex panel.
(20:22):
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
(20:45):
per sample.
So the cost is actually less than $100per sample. Wow.
Which means that it would be much easierto 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
(21:05):
as one tool in all the big populationhealth 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 Revealto make it super simple.
(21:26):
So you should be ableto just go in the lab
if you have a NGS sequencerand set it up and run it.
To get results really quickly.
So you can even run it manuallyor with a simple automation solution.
So no big investments to start up,but something that any genomics lab
already has.
(21:47):
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 exampleof how we democratize protein.
So that's great.
Yes it is, it is.
We've been waiting for this.
This is very exciting. Congratulations.
(22:08):
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 projectthat has been ongoing for quite some time
at R&D and we're super proud of this.
And really required a lot of dataanalysis of the data that are out there
(22:30):
that are publicly availablewhere we're allowed to go in and play with
and see what are the ones that havethe highest disease associations,
what seem most promisingfor having future disease associations,
where these cohorts just haven't been ableto afford to get into proteomics.
So I think this will offerquick publications.
And I think tracking the publicationsin review will be an exciting,
(22:53):
time to see labs
doing proteomicsthat have never even ventured in and then,
of course, the opportunity to validateorthogonally with mass spec
I think will be, also amazing.
That's a great point.
And I
think the choice of inflammationis really crucial
because inflammation, as all of usknow, is connected to our disease almost.
(23:16):
And that offers a possibilityfrom different types of researchers
for different diseaseareas to explore proteomics finally.
That's a great tool. Yeah.
I mean even in Alzheimer's disease, right.
Where there are clearly endotypesand some of them are associated
with information and some of them are not.
Being able to stratify
those patients in advanceof clinical trials, for example, might be
(23:38):
some application.
I know that several pharma have reportedthat, 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 proteinsusing a next generation sequencer.
And folks are seeing stratificationof these,
(24:00):
of these disease areasafter the clinical trial.
And they're seeing that these differentendo 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.
(24:20):
That would be really a great toolfor following protein biomarkers
really closely.
And I have a questionactually for both of you.
I think we’re discussing about now.
We discuss about tools that we’redeveloping now
with a perspective in the futureBut how do you see the future?
What do you see the challenges
and the wins we may havefrom the proteomics lab in the future?
I think Jenny first.
(24:42):
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 goingto see much more,
(25:05):
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.
(25:26):
So it's going to beI think we're going to see,
you know, also just on the first UKBstudy, we've already seen that happening.
That you're identifying these
really nice protein signatureswith a very strong
predictive power early on to say,you know,
if this patient will actually
(25:47):
get a certain diseaseseveral years in advance.
So I think it's going to be like,you know, from this discovery
to this more, clinical applicationsthat's going to happen quickly now.
We already had talked abouthow much has happened in a few years time.
Right.
So and just looking forwardand then in five years,
(26:08):
I can't even imagine,you know, what we're going to do.
but I think it's going to go like, youknow, it's going to be more multiomics.
It's going to be much more supportfor clinical proteomics.
And of course, we as a supplier,have a responsibility
to help that to happen.
Great, great.
Cindy, what do you think?
(26:29):
What is the future?
Well, I'll piggyback on somethingJenny said.
So thethe idea of being able to predict disease
many years in advance of getting disease.
I mean, that was really hot newswhen Keren Papier
and Ruth Travis and Karl Smith-Byrneand Josh Atkins, their paper came out.
There's, you know, seven plus years,it was a median of 12 years, 12 years
(26:51):
in many cancersof being able to predict disease.
And of course, those are those are many ofthose 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 getat a little bit of the detail there.
(27:13):
And I just sawthey had a preprint on prostate
cancer that characterizesome of the pathways in the immune system
that are predictive of a likelihoodof getting prostate cancer.
So I can't wait for thatto come out in publication.
So that brings up, the recent
announcementaround us running the entire UK Biobank.
That's 600,000 samples.
(27:34):
That's 500,000 individual with 100,000repeat samples at a 15 year mark.
Is my understanding,
being able to see that across
all of the diseases that are representedwithin 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 100,000that are followed up at 15 years.
(27:56):
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 around8,000
African diaspora samples, have around8,000 South Asian samples.
These are diverse, sets of samplesthat, as Rory Collins
(28:19):
says, aren't enough diversityfor us to really characterize everything.
But across the entire UKBiobank, we do, you know, effectively
have longitudinal representation,because if you get enough samples, you get
folksthat are in different stages of disease.
So though it isn't longitudinalin an individual, it can be, you know,
by being cross-sectionaland large enough in size can represent
(28:39):
some of the longitudinal aspects of
disease progression.
So that'swhat I'm really looking forward to.
And I expect those datato be published around 2027.
Which means that that the worldwill have access to
that international resource,
which is some of theand we talk about it as the UK Biobank,
(29:01):
but it is the internationally access to UKBiobank.
So it's an exciting time.
And just to add to thatthe statistical power of 600,000 samples,
would, you know, imaginewhat that would mean for understanding
rare diseases, for example,which hasn't been possible, really.
I mean, I know, we had that you could seethat also in the, in the sort of smaller
(29:25):
UKB, set from before,but with very few samples.
So I think that is also somethingthat is extremely important.
Already with these, the first papersthat we have or the small sample size
that you mentioned, Jenny.
Yeah, it's a small one.
I mean it we were able to seethere are great papers
(29:47):
from Claudia Langenberg, that we hadimprovement on diagnosis of disease.
Even better than clinical outcomessometimes.
And that was really impressive.
Well, it was really amazingfor 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 representationof those diseases. Exactly.
(30:09):
And ability to
you know, propose these protein scoresthat that
that will improve any over anythinga 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.
(30:29):
So with that, I will wrap up this episodeof, 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 trackthree and track four in Hall C,
(30:51):
and very closeto 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 folksthat are there.
Would be great.That would be a great event.
Really? Yeah. I wish you were here.
(31:12):
I wish I was there, but I’mlooking forward to hear your feedbacks.
I’m sure you have great feedbackfrom that.
Yeah, we could do an episode live.
Yeah,that would be great idea. Great idea.
All right.
Thankyou 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 Proteomicsin Proximity.
(31:35):
Huge thanks to our guests and authorsof such impactful publications.
I also want to thank you for tuning in.
Really appreciate you being here.
If you enjoyed the content of this
episode, please think about sharing itwith friends or colleagues
you think might be interestedin the content.
In addition, if you'd be willingto head over to Apple or Spotify
(31:56):
or wherever you digest your podcastsand give us a rating and review,
this will help others find the podcast
when they're searching for proteomicsor precision medicine podcasts.
And mostly I want to saywe would love to hear from you.
So we have a dedicated email address.
pip@olink.com please reach out.
Let us know what you're interestedin hearing, about what you care about
(32:18):
and any feedback on the episodesthat we have already done so far.
This is all about you,and so we're really keen
to make sure that we're meetingwhat you'd like to hear about.
Thank you so much, and we'll see you soon.
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