September 10, 2024 • 29 mins
Dr. Jonathan Hill, VP of Science and Technology and co-founder of Wasatch Biolabs joins the podcast today to talk about the future of genetic testing technology.
Dr. Hill shares his journey in genetics, starting from his undergraduate days to co-founding multiple biotech companies. They discuss the cutting-edge genomic and bioinformatic methods being developed at Wasatch Biolabs, including targeted DNA methylation sequencing and its significant implications for personalized medicine and early disease detection. Dr. Hill also elaborates on the integration of lab work and data analysis in genetic research, partnerships in diagnostic advancements, the role of AI in the future of genetic testing, and the importance of quality management in product development. Additionally, he speaks about the challenges faced in implementing these technologies and how they are being addressed. The conversation concludes with Dr. Hill's vision for the future of genetic testing and his approach to preparing students for the industry’s evolving landscape.
00:00 Introduction to the Episode
00:25 Meet Dr. Jonathan Hill
01:45 Dr. Hill's Journey in Genetics
03:27 Understanding DNA Methylation Sequencing
06:15 Applications in Personalized Medicine
15:06 Challenges in Implementing Assays
18:40 Future of Genetic Testing Technology
23:14 Importance of Collaborations and Partnerships
26:58 Fun and Personal Insights
27:43 Conclusion and Contact Information
More about Dr. Hill:
Dr. Hill is the VP of Science and Technology and a co-founder of Wasatch BioLabs, a biotechnology company committed to transforming the field of diagnostics. The company delivers reliable laboratory services, offering transparent testing and accurate results for biotech firms, patients, and research institutions.
https://www.wasatchbiolabs.com/
https://www.linkedin.com/in/jonathon-t-hill/
Qualio website:
https://www.qualio.com/
Previous episodes:
https://www.qualio.com/from-lab-to-launch-podcast
Apply to be on the show:
https://forms.gle/uUH2YtCFxJHrVGeL8
Music by keldez
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:01):
Hi there! Welcome to the FromLab to Launch podcast by Qualio,
where we share inspiring storiesfrom the people on the front
lines of life sciences.
Tune in and leave inspired tobring your life saving products
to the world.
Meg Sinclair (00:17):
Welcome to another episode of From Lab to Launch by
Qualio, where we delve into thelatest innovations and insights
from the life sciences industry.
I'm Meg, your host, and today wehave Dr.
Jonathan Hill, VP of Science andTechnology and co founder of
Wasatch Biolabs.
Dr.
Hill is not only a pioneer inthe field of biotechnology, but
also a distinguished researcherand educator with a Fulbright
(00:40):
scholarship and an early careerteaching at awards under his
belt.
You can get his full bio in theshow notes.
Thanks.
At Wasatch Biolabs, Dr.
Hill focuses on developingcutting edge genomic and
bioinformatic methods,particularly in the realm of
genetic and epigenetic research,aiming to revolutionize clinical
diagnostics.
(01:01):
Wasatch Biolabs is at theforefront of utilizing Oxford
nanopore technologies to provideadvanced DNA and RNA sequencing
services.
Their innovative approaches intargeted DNA methylation
sequencing have significantimplications for personalized
medicine and early diseasedetection, offering precise and
(01:22):
reliable diagnostic solutions.
Join us as we delve into thefuture of genetic testing
technology and explore howcombining traditional lab
methods with modernbioinformatics can lead to
groundbreaking advancements inhealthcare.
So let's dive in.
Welcome Dr.
Hill.
Dr. Jonathan Hill (01:40):
Wonderful.
Thanks for the nice intro andit's great to be here.
Meg Sinclair (01:43):
Thank you.
It's great to have you.
Can you tell us a little bitabout your journey in the field
of genetics and what inspiredyou to co found Wasatch Biolabs?
Dr. Jonathan Hill (01:52):
Yeah, I mean, that's, that's been a long
journey, uh, kind of.
Eased into it slowly over mycareer.
But, uh, you know, like mostpeople, when I was an
undergraduate in the biologyfield, I was planning to be a
doctor.
That was my goal.
I was pre-med and my junior yearI was TAing a genetics class and
I was doing research in a lab,uh, and I decided I really like
(02:14):
this life.
Um, I really think this is, uh,a cool kind of, uh, thing to do
and a great way to help people.
Uh, and that was my goal as adoctor too.
I wanted to help people and Ithought, you know.
Professors help people teaching,doing research, all those kinds
of things.
So I shifted gears suddenly,decided to go get a PhD instead,
um, and then got into theresearch.
(02:36):
And then the next phase wasgoing through my career.
Uh, you know, I would do someresearch, I would publish it.
And to be frank and honest, mostof that research sits on a shelf
and hardly anyone looks at it.
Right.
And so I started thinking abouthow can I actually make sure
that my research is getting outto people in ways that will
impact their lives.
(02:57):
And what I found is the way todo that is to.
So take it yourself to try totake the things that we're
learning in the lab.
Think about how can we do this,uh, in my case to do a
diagnostic or to help withprecision medicine, and then
actually work with businesspartners, et cetera, to
commercialize that technology.
Uh, and so I've actually beeninvolved with three companies
(03:18):
now.
Um, and it's always a funjourney.
It's exciting to do each time.
Meg Sinclair (03:24):
That's amazing.
Thank you for sharing thatjourney for our listeners.
Can you explain what targetedDNA methylation sequencing is
and its significance in clinicaldiagnostics?
Dr. Jonathan Hill (03:34):
Yeah, we're getting right into the weeds
here.
Um, so while the startbasically, uh, basic on here,
just for anyone that needs it,uh, starting about We had the
sequence of the human genome.
We knew all of the A T C's andG's that make up our DNA.
And that was a monumentaltransformation in our field to
(03:55):
be able to look at that DNA andunderstand it.
And several technologies gotdeveloped around that project
that help us sequence our DNA.
And so, you know, you have.
Better diagnostics.
You have, uh, ancestry typethings where people can look up
where they're from and whothey're related to.
We have all kinds of things thathave come out of that.
But one thing we learned is howmany things are not related to
(04:20):
the sequence in our DNA.
When they were doing the humangenome projects, there was a
belief out there that once wehad that we could explain any,
any disease because it was inour DNA.
And since that time we'velearned over the years, That a
lot of diseases aren't found inour DNA.
For example, I do a lot of workin congenital heart defects,
children that are born with somesort of problem in their heart
(04:43):
and genetics can only explainabout 20 to 30 percent of those
cases.
And so the question becomes,well, what else is going on?
And some of that'senvironmental.
Influences that we're gainingmore appreciation for and some
of it is what is calledepigenetics and the idea of
epigenetics is that your DNA andall of your cells is exactly the
(05:06):
same.
Right.
Same sequence, every single cellin your body, but your eye cells
are very different than your toecells.
Right.
Um, and so, uh, the differencescome because as an embryo is
developing and throughout ourlife and in response to
different disease states orenvironmental factors, our body
is making chemical modificationsto the DNA.
(05:28):
And those modifications are notstatic, they change between
cells, they change over time,you can actually estimate
someone's age by some of these,um, and they change in response
to different environmentalfactors or if you have a certain
disease.
And so what we're doing now iswe're using third generation
sequencing technology that cantell us not only the A, T, Cs
(05:49):
and Gs, but also what chemicalmodifications.
Are made to these, and we can douse that to diagnose things like
neurodegeneration or, um,several fertility tests, looking
at the quality of the sperm,things like that, by looking at
these chemical modifications, wecan gain new insights into our
health and what's happening withus.
Meg Sinclair (06:13):
Thank you for that wonderful explanation.
So how does combining specificDNA tests with genetic
sequencing improve the accuracyand effectiveness for
personalized medicine?
Dr. Jonathan Hill (06:24):
Yeah, so the idea is.
Like I was mentioning, we canuse these to look at someone's,
you know, health state, or onething that we're doing a lot of
is looking for evidence of dyingcells.
Okay.
And how this works is when acell dies, it spills its DNA
into the blood and the blood,uh, ends up degrading that DNA
(06:45):
and cleaning it out.
But for a certain amount oftime, it's floating around in
your blood and what is calledcell free DNA.
Cause it's not inside any cells.
We collect that.
And we're not looking at the DNAsequence per se, um, but we're
looking for the chemicalmodifications tied to that DNA
sequence.
And by looking for specific DNAsequences, or methylation,
(07:08):
chemical modifications, we cantell where that DNA came from.
So, for example, someone mighthave Alzheimer's, and they've
got a lot of cortical neurondeath in their brain.
Well, the cortical neurons haveparticular sequences of DNA that
have specific chemicalmodifications, and if we can
measure that in the blood, weknow those cells are dying, and
(07:30):
we can measure the rate ofdeath, um, and if they get drug
treatments, we can actually seeif that goes away, if the drug
treatment is working, right?
So we can use that as abiomarker to first diagnose the
disease.
And then to look at thetreatments that are being given
to see how effective they are.
And that's where the kind ofprecision medicine gets in where
(07:50):
you're not just taking a drugand hoping, but you can actually
measure its effects and, uh,change the treatment if you need
to, because it's not beingeffective.
Meg Sinclair (08:00):
That's I love that personalized approach to, um,
yeah, and taking the hope out ofit and having a more
personalized approach.
That's great.
What are other potentialapplications of your DNA
analysis techniques in earlydisease detection and how can
they change current diagnosticpractices?
Dr. Jonathan Hill (08:18):
Oh, well, okay.
Yeah, I'm going to start narrowand then I want to broaden out a
little bit there if that's okay.
Um, first of all, I, any celldeath we can measure.
Okay.
So you can think of autoimmunediseases, uh, you can think of
arthritis, you can think ofinflammatory diseases,
neurodegeneration, all thetypes, not just Alzheimer's, all
anything that's got a specificcell type that is dying, we can
(08:42):
measure that rate of death.
Um, and often this signal comesup long before you have
symptoms.
Um, we're still very much inresearch.
It's not out yet, but some ofour preliminary results have
shown that people that weretested five years ago and had
their blood stored.
Um, and then now have beendiagnosed with Alzheimer's five
(09:03):
years later, we can take theirblood from five years ago and
see the signal already.
And so at least five yearsbefore onset of symptoms, before
anyone knows anything's goingon, the biological processes are
already happening and we canmeasure that very, very
sensitively and get a very earlyindication.
And of course, that's huge,right?
The earlier we can find things,the easier they are to treat.
(09:26):
As far as how this might changediagnostic practices, I think
there's kind of two main ways.
First of all, it means some ofthese serious diseases, uh, we
can start screening for, right?
Regularly.
Uh, the test is fairlyinexpensive.
You could do it as part of yourannual physical, for example,
and we hope that this becomes anormal part.
(09:48):
You get this biomarker measuredand the doctor can get insights
that they may not have access tootherwise.
Right.
And I think this plays into abigger picture.
That in my mind, one of the biglimitations of medicine right
now Is that we do not usediagnostics enough.
There's too much of the guessand check, right?
(10:11):
Just to give my own personalexperience with this.
This is kind of a silly story,but it illustrates it.
Well, um, I had kind of anathlete's foot type infection.
My feet were itching like crazy,uh, kind of embarrassing to say,
I know, but that's whathappened.
And, but you know, the overcounter athlete's foot
treatments were not working.
I went into a dermatologist andhe talks to me for a couple of
(10:33):
minutes and then says, whydidn't you try this cream?
So I go to the pharmacy, buy thecream, it's kind of expensive,
but it's worth it.
I try it for two weeks,nothing's working.
So I go back in, I have to setup another appointment, go back
in, um, I still have thediscomfort, everything.
And he goes, huh, that's odd,try this cream.
And then I go home and I try itfor another two weeks, nothing's
(10:57):
working, it's not helping.
Set up another appointment, goback in, And he goes, Oh, I
thought one of those would haveworked.
And then he takes some scrapingsof my skin, goes to a microscope
in the back room, looks at itand goes, Oh, I know what you
have.
Here's what you need.
And I look back at that and I'mlike, and that one worked,
right?
Everything was cured in a fewdays.
Everything was good.
(11:18):
And I'm like, why did that takeme three visits and three
different creams before I gotthe correct one?
Right.
Why, why did we not do thediagnostic upfront?
No, what it is.
and be done.
That would make our health caresystem so much more efficient
than it is right now.
(11:38):
And then on the flip side, wekeep finding that drugs work for
80 percent of patients, right?
For whatever disease kind ofthing.
And some of these are quiteexpensive treatments that we're
doing for cancer andneurodegeneration, those kinds
of things.
What if we could measure andreally know if that drug is
working and more quickly pivotto something new before we've
(12:02):
had a significant decline thatcan be measured in the clinic.
Right.
Um, again, we would make thingsmore efficient.
We would waste less time, money,resources, treating something
Incorrectly.
And I think if we look at ourfield of medicine right now,
that is something that we haveto figure out.
We have a chronic shortage ofdoctors.
Uh, we have, uh, access issuesto healthcare.
(12:25):
Those can be addressed by makingthe system more efficient.
We view our technology as partof it.
One little piece of it.
Meg Sinclair (12:33):
No, but in a very important piece for that
personalized medicine and thatearly detection is going to be
huge and in creatingefficiencies in our health care
system where we're doing primaryinterventions and not secondary
or tertiary treatments.
So, it's very important.
How does integrating lab work,data analysis, and genetics help
(12:54):
us understand gene behavior morecomprehensively?
Dr. Jonathan Hill (12:57):
Oh, that's an interesting question.
Um, I mean, a lot of that's alittle esoteric, right?
The little bit of details, uh,but I specifically and, uh, some
of my other co founders, we'vereally made a career, all of us
of being able to do the work atthe bench and the genomics and
the data analysis, and that hasbeen huge to helping us find
(13:20):
insights that we would not have.
Otherwise, right?
Too often in our field, you'vegot the bench biologist and then
you have the data analyst andthey really can't even talk to
each other often, right?
And that creates a problem wheremaybe the bench biologist isn't
designing experiments for thebest data analysis or the data
(13:40):
analyst isn't realizing that.
There is an explanation for acertain source of noise in their
data because they're notexperienced enough with the
collection of that data right tosee that, um, and so you gain
interesting insights bycombining the two.
And it's something that I tellmy students.
All the time that they need tomake sure they're doing in their
(14:00):
career.
Biology used to be the scienceyou went to if you didn't like
math and computers.
Right.
It was kind of the artsyscience, if you will, you drew
pictures.
Um, and that's no longer thecase.
And so we have to increase theclasses that we teach, um, the,
the experiences we give, thingslike that on.
Data and, uh, analysis alongwith the bench work and the
(14:24):
experiments that we're doing sothat we can tie those two
together and really help us.
Um, we've, as we've developedthese diagnostic tests, uh, it's
been huge to be able to go backand forth and say, Oh, the data
is showing this.
What if we tweaked this aboutthe actual processing of the
samples, right?
To improve that and back andforth.
(14:46):
Kind of thing and so that'sthat's been a huge benefit to
the development that we've done
Meg Sinclair (14:52):
and it sounds like educating a new generation of
data Analysts biologists too.
So that's amazing work.
You've been doing over therealways we
Dr. Jonathan Hill (15:00):
got to do it because that's where the world's
going, right?
Everything's gonna be AI in 10years.
So we got to have them ready.
Meg Sinclair (15:06):
Yes, they better be ready So what are some
challenges that you'veencountered while implementing
target methylation assays andhow have you addressed them?
Dr. Jonathan Hill (15:17):
Yeah, uh, there have been many.
It always takes longer than youhope, right?
We set this up two years ago.
Naively, we're like, hey, we'resix months out.
We've got good preliminary data.
And what the extra year and ahalf has been.
We're just now getting to thepoint where we're really close
to launching, we think.
But again, who knows?
Could run into more bumps andit's centered around consistency
(15:40):
and quality of the stuff thatwe're getting.
We can do it in the lab in avery low throughput kind of way
with very controlled samples.
But now as you seek to move thisinto a kind of a production
phase, you have to make surethat assay works almost every
single time that you canidentify when it didn't work,
right?
Um, and what went wrong.
(16:01):
Um, and then you have to be ableto work with samples that came
and they weren't quite collectedcorrectly, or they weren't
stored correctly, and all thesekinds of variables that start
showing up that you never hadwhen you were processing
everything yourself, right?
And trying to smooth that outand make that work has been kind
(16:22):
of a big hurdle for us.
Um, and kind of an adventure,but we're getting there.
We've the assay now looks verydifferent than it did.
And all of that is looking forconsistency and throughput and
all those kinds of factors.
Meg Sinclair (16:36):
I'm very glad, glad you brought up quality.
Dr.
Hill here at qualia, we providea digital EQMS system.
So quality is near and dear toour hearts.
Um, so we love to ask foundershow you approach quality
management and your productdevelopment.
Dr. Jonathan Hill (16:51):
Well, yeah, that's something we're still
figuring out.
It is a work in progress, right?
One of the hard things we've hadis.
For example, as we go for theclinical certifications, they
want to know, Hey, whatthreshold of this parameter, uh,
in your data, would you fail thetest?
Right.
And that means we have to runenough to really know.
(17:13):
And usually in our case, we havesome that are really bad and
they're obvious and the restlook really good.
And there's this huge chasm inbetween.
And trying to figure out, okay,where do I draw the threshold in
that huge chasm so that I'm notfailing tests that are fine
because that costs money, butI'm also not passing tests that
failed, uh, can be kind of achallenge as you're starting up
(17:34):
and getting things going.
And so I think there's some keythings.
One is.
We've actually gotten to thepoint is as we're running
samples, we don't, we don'tthrow out samples that aren't
looking good.
We run them anyway, just to seewhat the data is going to look
like when we know it didn't goso good.
Right.
Uh, so collecting that data isimportant.
Then establishing goodmeasurable thresholds along the
(17:57):
way.
That we can use in production.
And then the final step isfrankly going to be in the early
stages.
We plan on partnering withpeople doing clinical trials and
things like that, kind of aresearch application first, so
that we can track it in the realworld and be tracking our
performance as we go so that wecan go back and say, okay, we
(18:18):
need to modify.
Uh, this quality parameter, ormaybe we can tweak the test here
because we have a high fail ratethat we can track to that one
step, things like that.
And we'll never get the numberswe need for that until we're
actually out there kind ofrunning with early adopters.
Meg Sinclair (18:36):
Great.
Well, I can't wait to see thatcome to fruition for you all.
How do you envision the futureof genetic testing technology
evolving in the next decade?
I know we talked a little bitabout AI, but what else do you
see on the horizon there?
Dr. Jonathan Hill (18:49):
Yeah, well, AI is what I see and how I see
it applied.
is, you know, everybody now haselectronic health records.
We're getting to the point whereyou can get a genome sequenced
for less than a thousanddollars, which for clinical test
standards is quite cheap.
Um, that's the raw data cost.
Um, and you only need that oneonce cause that never changes.
(19:12):
We and others are trying to Uh,develop new diagnostic tests
that can inform these models,and I really see over the next
10 years the development of adiagnostic AI, a tool that
doctors can use that can lookinto all of these different
kinds of inputs in history ofthe patient and make a very
(19:33):
informed decision.
Um, diagnosis and you know, it'sinteresting if you think about
your doctor, often they startgetting really good at
diagnosing the things that theysee all the time, right?
And you know, you get the doctorthat in winter is like, Oh,
that's this because I've seen 20cases of this already.
I know it's going around RSV orwhatever, and I can recognize it
(19:56):
now, right?
So imagine a doctor that had theequivalent of 10, 000 years of
clinical experience and amillion patients.
Right?
What kind of diagnoses couldthey make?
Maybe that aren't so common, buta rare, but once you start
thinking about those numbers.
Become enough that you can seethose patterns.
That's what diagnostic AI canbecome.
(20:16):
We can have a million recordsfrom different people of all
different walks of life, buthave all the data there train
models that can even recognizerare disease.
And really help doctors out.
I think doctors will still havea role.
We're not going to, they're notgoing to get replaced yet.
That might be 50 years orsomething, but 10 years, they're
still here.
(20:37):
Because that connection with thepatient, the ability to explain
things to the patient, theability to have some intuition
on whether a particular patientwill follow a treatment plan
well, or, you know, family andsocial factors might be involved
there are still important.
And we, as humans have greatintuition.
They're right, but the diagnosisitself is better suited for a
(21:01):
computer than a human.
You're taking lots of datainputs, finding complex patterns
in those and then combining themto get.
That also means that over thenext 10 years, I hope to see a
shift in my field in thediagnostic space, looking more
at our diagnostics, not just asan answer, right?
(21:23):
Right now, when you get adiagnostic, you want the bright
blue band to show up.
Um, as you have COVID, forexample, right?
You want it to be the answer.
And that's quite limiting.
But if we look at it as this isa data point that will be
combined with a whole bunch ofother things like their genome
sequence, et cetera, or imagingresults or those kinds of
(21:46):
things, and think of it not asone single standalone answer,
but one part of a big puzzlethat will make our, our
diagnostic work much morepowerful.
Meg Sinclair (21:59):
How are you preparing your students for, for
the future decades to come in,in the industry?
Dr. Jonathan Hill (22:06):
Well, likely, I think.
Yeah, I think that's a big partof it.
We talked about teaching themdata analysis skills, right?
Trying to think that way.
We don't think in biology oftenin a big data kind of way, just
the way we frame our questions,things like that.
Trying to get the students tostart seeing things from that
different paradigm.
But the biggest thing that I tryto give them in all my classes,
(22:28):
Is the tools to learn on theirown after they leave the class,
because, you know, we can sithere and say, Oh, 10 years.
I think it's going to look likethis, but we don't really know,
uh, where it's going to go.
It what we do know is it's goingto advance and change a lot,
right?
And if they're thinking thatthey're done learning everything
when they finish medical school,for example, they're completely
(22:51):
wrong because the field is goingto change several times
throughout their career, andthey have to have the skill set
to pick those up.
And learn as they go to, to makeeffective use of these new tools
as they come on.
Meg Sinclair (23:05):
Yeah, I think that's good advice for students
in any field, um, in our everchanging landscape, um, with
technology.
What role do collaborations andpartnerships play in your
research and developmentefforts?
And can you share any excitingprojects you currently have
underway?
Dr. Jonathan Hill (23:22):
Yeah, I can.
So, uh, collaborations are sohuge for us.
First of all.
Uh, there's kind of twoscientific leads in our company,
me and Tim Jenkins were bothprofessors at BYU and this
entire company came out ofcollaborations that we have.
And we always talk about thesynergy of our skill sets, how
(23:43):
there are things that we've beenable to do that I never would
have thought of, but I had theskill set to answer and just
didn't realize it.
Um, and things that he had theskill set to do.
Um, but I didn't know how toanswer the question.
Right.
And so, uh, by working togetherwithin a team, we can get those.
And of course, with any companythat expands out, um, I like to
think I'm pretty good atdeveloping these techniques,
(24:05):
working on the bioinformaticsand the bench, those kinds of
things.
I have no clue how to run acompany.
I don't know how to do series afunding.
And they throw out all theseterms that I don't even know
what they are.
Right.
There's a whole nother sidethere that you have to do.
Thanks.
And then within our company,we've realized that we've built
this platform for measuring thechemical modifications of the
(24:27):
DNA in a diagnostic space.
But, you know, we're not expertsin all of these diseases.
And so again, we have blindspots.
We won't even see the potentialapplications of this in many
cases, but others will.
And so right now we have severalcustomers who really are
companies.
Who have been working on acertain diagnostic or in a
(24:49):
certain space and say, look atour technology and say, Hey,
that might solve a problem thatI have.
And then we work together on it,right?
And they provide the intuitionfor the specific application.
We know the technology insideand out.
And together we can developthat.
So we're always looking for newpartners in that kind of way.
Um, our first clinical kind ofapproval.
(25:11):
Application are clear.
Application is going in rightnow.
That was done with a partnercompany working in the fertility
space.
Uh, and in this case, they'vedeveloped a test that will tell
you if a certain fertilitytreatment will work or not.
And so that's great for people.
These are often expensiveinsurance often doesn't cover
him.
So to know ahead of time if thishas a chance.
Uh, is a good thing.
(25:33):
Um, they have a couple othertests that they're developing as
well, related to fertility andprostate cancer, things like
that, we have another companythat we've partnered with, uh,
that is working on theAlzheimer's diagnostic, the
neurodegeneration right now,we're trying to expand it out
from just Alzheimer's toAlzheimer's Parkinson's ALS that
(25:53):
work is ongoing, but lookingvery promising, uh, we have
another one that's looking at itfor looking at Lyme's disease.
Seeing if we can diagnose thathard to diagnose disease much
better with our technology.
And then finally we have a neatone because the company started
from a student in one of ourlabs here.
Um, and she realized that shecould use this technology for a
(26:17):
woman's health typeapplications.
Uh, and so she's taken andcreated her own company and is
developing, uh, using ourtechnology, her own set of
tests, all related to women'shealth.
And applying it there.
And so we like to help peopleget going.
We like to help them solve theirproblems.
Uh, and we see so many newapplications that can come with
(26:40):
new partnerships in the future.
Meg Sinclair (26:43):
Sounds like a lot of exciting partnerships.
I can't wait to see who else youpartner with in the near future.
Dr. Jonathan Hill (26:49):
It's fun.
We don't like working alone.
That's boring.
Meg Sinclair (26:53):
That's great.
The more the merrier.
Dr. Jonathan Hill (26:56):
Yeah, exactly.
Meg Sinclair (26:58):
Um, and our last question to finish us off is
more of a fun one.
We love to ask each of ourguests if we ran into at the
bookstore or at the BYU libraryin which section would we find
you?
Dr. Jonathan Hill (27:10):
Oh, me personally?
Meg Sinclair (27:12):
I will
Dr. Jonathan Hill (27:12):
admit most of my reading right now is
scientific papers.
The technology is moving so fastthat I just have to keep up with
what's going on.
Right.
Uh, but I do enjoy kind ofclassical fantasy kind of stuff,
Lord of the Rings fan.
Uh, so you'd probably find me inthat section if it's a day where
I feel like I'm actually caughtup for once on my scientific
(27:35):
reading.
Meg Sinclair (27:36):
Or needed to escape from all the
bioinformatics
Dr. Jonathan Hill (27:40):
need a break.
Yep.
Meg Sinclair (27:43):
Lovely.
Well, thank you so much forjoining us.
Dr.
Hill.
Where can those go?
Who wants to follow along withyour journey and find out more
and connect with you?
Dr. Jonathan Hill (27:51):
You can find me on LinkedIn.
I've got a profile there.
And then also you can find ourcompany and what we're doing at
wasatchbiolabs.
com.
Meg Sinclair (27:59):
Great.
We'll get those posted in theshow notes for our listener.
It was great having you today.
Dr.
Hill.
Thank you so much.
Dr. Jonathan Hill (28:06):
Thanks for having me.
Thank you for listening to thisweek's episode of From Lab to
Launch, brought to you byQualio.
If you like what you've heard,please subscribe and give the
(28:27):
show a positive review.
It really helps us out.
For more information aboutQualio, our guest today, or to
be a guest on a future episode,please refer to the show notes.
Until next time.
Hi there! Welcome to the FromLab to Launch podcast by Qualio,
where we share inspiring storiesfrom the people on the front
lines of life sciences.
Tune in and leave inspired tobring your life saving products
to the world.
Meg Sinclair (00:17):
Welcome to another episode of From Lab to Launch by
Qualio, where we delve into thelatest innovations and insights
from the life sciences industry.
I'm Meg, your host, and today wehave Dr.
Jonathan Hill, VP of Science andTechnology and co founder of
Wasatch Biolabs.
Dr.
Hill is not only a pioneer inthe field of biotechnology, but
also a distinguished researcherand educator with a Fulbright
(00:40):
scholarship and an early careerteaching at awards under his
belt.
You can get his full bio in theshow notes.
Thanks.
At Wasatch Biolabs, Dr.
Hill focuses on developingcutting edge genomic and
bioinformatic methods,particularly in the realm of
genetic and epigenetic research,aiming to revolutionize clinical
diagnostics.
(01:01):
Wasatch Biolabs is at theforefront of utilizing Oxford
nanopore technologies to provideadvanced DNA and RNA sequencing
services.
Their innovative approaches intargeted DNA methylation
sequencing have significantimplications for personalized
medicine and early diseasedetection, offering precise and
(01:22):
reliable diagnostic solutions.
Join us as we delve into thefuture of genetic testing
technology and explore howcombining traditional lab
methods with modernbioinformatics can lead to
groundbreaking advancements inhealthcare.
So let's dive in.
Welcome Dr.
Hill.
Dr. Jonathan Hill (01:40):
Wonderful.
Thanks for the nice intro andit's great to be here.
Meg Sinclair (01:43):
Thank you.
It's great to have you.
Can you tell us a little bitabout your journey in the field
of genetics and what inspiredyou to co found Wasatch Biolabs?
Dr. Jonathan Hill (01:52):
Yeah, I mean, that's, that's been a long
journey, uh, kind of.
Eased into it slowly over mycareer.
But, uh, you know, like mostpeople, when I was an
undergraduate in the biologyfield, I was planning to be a
doctor.
That was my goal.
I was pre-med and my junior yearI was TAing a genetics class and
I was doing research in a lab,uh, and I decided I really like
(02:14):
this life.
Um, I really think this is, uh,a cool kind of, uh, thing to do
and a great way to help people.
Uh, and that was my goal as adoctor too.
I wanted to help people and Ithought, you know.
Professors help people teaching,doing research, all those kinds
of things.
So I shifted gears suddenly,decided to go get a PhD instead,
um, and then got into theresearch.
(02:36):
And then the next phase wasgoing through my career.
Uh, you know, I would do someresearch, I would publish it.
And to be frank and honest, mostof that research sits on a shelf
and hardly anyone looks at it.
Right.
And so I started thinking abouthow can I actually make sure
that my research is getting outto people in ways that will
impact their lives.
(02:57):
And what I found is the way todo that is to.
So take it yourself to try totake the things that we're
learning in the lab.
Think about how can we do this,uh, in my case to do a
diagnostic or to help withprecision medicine, and then
actually work with businesspartners, et cetera, to
commercialize that technology.
Uh, and so I've actually beeninvolved with three companies
(03:18):
now.
Um, and it's always a funjourney.
It's exciting to do each time.
Meg Sinclair (03:24):
That's amazing.
Thank you for sharing thatjourney for our listeners.
Can you explain what targetedDNA methylation sequencing is
and its significance in clinicaldiagnostics?
Dr. Jonathan Hill (03:34):
Yeah, we're getting right into the weeds
here.
Um, so while the startbasically, uh, basic on here,
just for anyone that needs it,uh, starting about We had the
sequence of the human genome.
We knew all of the A T C's andG's that make up our DNA.
And that was a monumentaltransformation in our field to
(03:55):
be able to look at that DNA andunderstand it.
And several technologies gotdeveloped around that project
that help us sequence our DNA.
And so, you know, you have.
Better diagnostics.
You have, uh, ancestry typethings where people can look up
where they're from and whothey're related to.
We have all kinds of things thathave come out of that.
But one thing we learned is howmany things are not related to
(04:20):
the sequence in our DNA.
When they were doing the humangenome projects, there was a
belief out there that once wehad that we could explain any,
any disease because it was inour DNA.
And since that time we'velearned over the years, That a
lot of diseases aren't found inour DNA.
For example, I do a lot of workin congenital heart defects,
children that are born with somesort of problem in their heart
(04:43):
and genetics can only explainabout 20 to 30 percent of those
cases.
And so the question becomes,well, what else is going on?
And some of that'senvironmental.
Influences that we're gainingmore appreciation for and some
of it is what is calledepigenetics and the idea of
epigenetics is that your DNA andall of your cells is exactly the
(05:06):
same.
Right.
Same sequence, every single cellin your body, but your eye cells
are very different than your toecells.
Right.
Um, and so, uh, the differencescome because as an embryo is
developing and throughout ourlife and in response to
different disease states orenvironmental factors, our body
is making chemical modificationsto the DNA.
(05:28):
And those modifications are notstatic, they change between
cells, they change over time,you can actually estimate
someone's age by some of these,um, and they change in response
to different environmentalfactors or if you have a certain
disease.
And so what we're doing now iswe're using third generation
sequencing technology that cantell us not only the A, T, Cs
(05:49):
and Gs, but also what chemicalmodifications.
Are made to these, and we can douse that to diagnose things like
neurodegeneration or, um,several fertility tests, looking
at the quality of the sperm,things like that, by looking at
these chemical modifications, wecan gain new insights into our
health and what's happening withus.
Meg Sinclair (06:13):
Thank you for that wonderful explanation.
So how does combining specificDNA tests with genetic
sequencing improve the accuracyand effectiveness for
personalized medicine?
Dr. Jonathan Hill (06:24):
Yeah, so the idea is.
Like I was mentioning, we canuse these to look at someone's,
you know, health state, or onething that we're doing a lot of
is looking for evidence of dyingcells.
Okay.
And how this works is when acell dies, it spills its DNA
into the blood and the blood,uh, ends up degrading that DNA
(06:45):
and cleaning it out.
But for a certain amount oftime, it's floating around in
your blood and what is calledcell free DNA.
Cause it's not inside any cells.
We collect that.
And we're not looking at the DNAsequence per se, um, but we're
looking for the chemicalmodifications tied to that DNA
sequence.
And by looking for specific DNAsequences, or methylation,
(07:08):
chemical modifications, we cantell where that DNA came from.
So, for example, someone mighthave Alzheimer's, and they've
got a lot of cortical neurondeath in their brain.
Well, the cortical neurons haveparticular sequences of DNA that
have specific chemicalmodifications, and if we can
measure that in the blood, weknow those cells are dying, and
(07:30):
we can measure the rate ofdeath, um, and if they get drug
treatments, we can actually seeif that goes away, if the drug
treatment is working, right?
So we can use that as abiomarker to first diagnose the
disease.
And then to look at thetreatments that are being given
to see how effective they are.
And that's where the kind ofprecision medicine gets in where
(07:50):
you're not just taking a drugand hoping, but you can actually
measure its effects and, uh,change the treatment if you need
to, because it's not beingeffective.
Meg Sinclair (08:00):
That's I love that personalized approach to, um,
yeah, and taking the hope out ofit and having a more
personalized approach.
That's great.
What are other potentialapplications of your DNA
analysis techniques in earlydisease detection and how can
they change current diagnosticpractices?
Dr. Jonathan Hill (08:18):
Oh, well, okay.
Yeah, I'm going to start narrowand then I want to broaden out a
little bit there if that's okay.
Um, first of all, I, any celldeath we can measure.
Okay.
So you can think of autoimmunediseases, uh, you can think of
arthritis, you can think ofinflammatory diseases,
neurodegeneration, all thetypes, not just Alzheimer's, all
anything that's got a specificcell type that is dying, we can
(08:42):
measure that rate of death.
Um, and often this signal comesup long before you have
symptoms.
Um, we're still very much inresearch.
It's not out yet, but some ofour preliminary results have
shown that people that weretested five years ago and had
their blood stored.
Um, and then now have beendiagnosed with Alzheimer's five
(09:03):
years later, we can take theirblood from five years ago and
see the signal already.
And so at least five yearsbefore onset of symptoms, before
anyone knows anything's goingon, the biological processes are
already happening and we canmeasure that very, very
sensitively and get a very earlyindication.
And of course, that's huge,right?
The earlier we can find things,the easier they are to treat.
(09:26):
As far as how this might changediagnostic practices, I think
there's kind of two main ways.
First of all, it means some ofthese serious diseases, uh, we
can start screening for, right?
Regularly.
Uh, the test is fairlyinexpensive.
You could do it as part of yourannual physical, for example,
and we hope that this becomes anormal part.
(09:48):
You get this biomarker measuredand the doctor can get insights
that they may not have access tootherwise.
Right.
And I think this plays into abigger picture.
That in my mind, one of the biglimitations of medicine right
now Is that we do not usediagnostics enough.
There's too much of the guessand check, right?
(10:11):
Just to give my own personalexperience with this.
This is kind of a silly story,but it illustrates it.
Well, um, I had kind of anathlete's foot type infection.
My feet were itching like crazy,uh, kind of embarrassing to say,
I know, but that's whathappened.
And, but you know, the overcounter athlete's foot
treatments were not working.
I went into a dermatologist andhe talks to me for a couple of
(10:33):
minutes and then says, whydidn't you try this cream?
So I go to the pharmacy, buy thecream, it's kind of expensive,
but it's worth it.
I try it for two weeks,nothing's working.
So I go back in, I have to setup another appointment, go back
in, um, I still have thediscomfort, everything.
And he goes, huh, that's odd,try this cream.
And then I go home and I try itfor another two weeks, nothing's
(10:57):
working, it's not helping.
Set up another appointment, goback in, And he goes, Oh, I
thought one of those would haveworked.
And then he takes some scrapingsof my skin, goes to a microscope
in the back room, looks at itand goes, Oh, I know what you
have.
Here's what you need.
And I look back at that and I'mlike, and that one worked,
right?
Everything was cured in a fewdays.
Everything was good.
(11:18):
And I'm like, why did that takeme three visits and three
different creams before I gotthe correct one?
Right.
Why, why did we not do thediagnostic upfront?
No, what it is.
and be done.
That would make our health caresystem so much more efficient
than it is right now.
(11:38):
And then on the flip side, wekeep finding that drugs work for
80 percent of patients, right?
For whatever disease kind ofthing.
And some of these are quiteexpensive treatments that we're
doing for cancer andneurodegeneration, those kinds
of things.
What if we could measure andreally know if that drug is
working and more quickly pivotto something new before we've
(12:02):
had a significant decline thatcan be measured in the clinic.
Right.
Um, again, we would make thingsmore efficient.
We would waste less time, money,resources, treating something
Incorrectly.
And I think if we look at ourfield of medicine right now,
that is something that we haveto figure out.
We have a chronic shortage ofdoctors.
Uh, we have, uh, access issuesto healthcare.
(12:25):
Those can be addressed by makingthe system more efficient.
We view our technology as partof it.
One little piece of it.
Meg Sinclair (12:33):
No, but in a very important piece for that
personalized medicine and thatearly detection is going to be
huge and in creatingefficiencies in our health care
system where we're doing primaryinterventions and not secondary
or tertiary treatments.
So, it's very important.
How does integrating lab work,data analysis, and genetics help
(12:54):
us understand gene behavior morecomprehensively?
Dr. Jonathan Hill (12:57):
Oh, that's an interesting question.
Um, I mean, a lot of that's alittle esoteric, right?
The little bit of details, uh,but I specifically and, uh, some
of my other co founders, we'vereally made a career, all of us
of being able to do the work atthe bench and the genomics and
the data analysis, and that hasbeen huge to helping us find
(13:20):
insights that we would not have.
Otherwise, right?
Too often in our field, you'vegot the bench biologist and then
you have the data analyst andthey really can't even talk to
each other often, right?
And that creates a problem wheremaybe the bench biologist isn't
designing experiments for thebest data analysis or the data
(13:40):
analyst isn't realizing that.
There is an explanation for acertain source of noise in their
data because they're notexperienced enough with the
collection of that data right tosee that, um, and so you gain
interesting insights bycombining the two.
And it's something that I tellmy students.
All the time that they need tomake sure they're doing in their
(14:00):
career.
Biology used to be the scienceyou went to if you didn't like
math and computers.
Right.
It was kind of the artsyscience, if you will, you drew
pictures.
Um, and that's no longer thecase.
And so we have to increase theclasses that we teach, um, the,
the experiences we give, thingslike that on.
Data and, uh, analysis alongwith the bench work and the
(14:24):
experiments that we're doing sothat we can tie those two
together and really help us.
Um, we've, as we've developedthese diagnostic tests, uh, it's
been huge to be able to go backand forth and say, Oh, the data
is showing this.
What if we tweaked this aboutthe actual processing of the
samples, right?
To improve that and back andforth.
(14:46):
Kind of thing and so that'sthat's been a huge benefit to
the development that we've done
Meg Sinclair (14:52):
and it sounds like educating a new generation of
data Analysts biologists too.
So that's amazing work.
You've been doing over therealways we
Dr. Jonathan Hill (15:00):
got to do it because that's where the world's
going, right?
Everything's gonna be AI in 10years.
So we got to have them ready.
Meg Sinclair (15:06):
Yes, they better be ready So what are some
challenges that you'veencountered while implementing
target methylation assays andhow have you addressed them?
Dr. Jonathan Hill (15:17):
Yeah, uh, there have been many.
It always takes longer than youhope, right?
We set this up two years ago.
Naively, we're like, hey, we'resix months out.
We've got good preliminary data.
And what the extra year and ahalf has been.
We're just now getting to thepoint where we're really close
to launching, we think.
But again, who knows?
Could run into more bumps andit's centered around consistency
(15:40):
and quality of the stuff thatwe're getting.
We can do it in the lab in avery low throughput kind of way
with very controlled samples.
But now as you seek to move thisinto a kind of a production
phase, you have to make surethat assay works almost every
single time that you canidentify when it didn't work,
right?
Um, and what went wrong.
(16:01):
Um, and then you have to be ableto work with samples that came
and they weren't quite collectedcorrectly, or they weren't
stored correctly, and all thesekinds of variables that start
showing up that you never hadwhen you were processing
everything yourself, right?
And trying to smooth that outand make that work has been kind
(16:22):
of a big hurdle for us.
Um, and kind of an adventure,but we're getting there.
We've the assay now looks verydifferent than it did.
And all of that is looking forconsistency and throughput and
all those kinds of factors.
Meg Sinclair (16:36):
I'm very glad, glad you brought up quality.
Dr.
Hill here at qualia, we providea digital EQMS system.
So quality is near and dear toour hearts.
Um, so we love to ask foundershow you approach quality
management and your productdevelopment.
Dr. Jonathan Hill (16:51):
Well, yeah, that's something we're still
figuring out.
It is a work in progress, right?
One of the hard things we've hadis.
For example, as we go for theclinical certifications, they
want to know, Hey, whatthreshold of this parameter, uh,
in your data, would you fail thetest?
Right.
And that means we have to runenough to really know.
(17:13):
And usually in our case, we havesome that are really bad and
they're obvious and the restlook really good.
And there's this huge chasm inbetween.
And trying to figure out, okay,where do I draw the threshold in
that huge chasm so that I'm notfailing tests that are fine
because that costs money, butI'm also not passing tests that
failed, uh, can be kind of achallenge as you're starting up
(17:34):
and getting things going.
And so I think there's some keythings.
One is.
We've actually gotten to thepoint is as we're running
samples, we don't, we don'tthrow out samples that aren't
looking good.
We run them anyway, just to seewhat the data is going to look
like when we know it didn't goso good.
Right.
Uh, so collecting that data isimportant.
Then establishing goodmeasurable thresholds along the
(17:57):
way.
That we can use in production.
And then the final step isfrankly going to be in the early
stages.
We plan on partnering withpeople doing clinical trials and
things like that, kind of aresearch application first, so
that we can track it in the realworld and be tracking our
performance as we go so that wecan go back and say, okay, we
(18:18):
need to modify.
Uh, this quality parameter, ormaybe we can tweak the test here
because we have a high fail ratethat we can track to that one
step, things like that.
And we'll never get the numberswe need for that until we're
actually out there kind ofrunning with early adopters.
Meg Sinclair (18:36):
Great.
Well, I can't wait to see thatcome to fruition for you all.
How do you envision the futureof genetic testing technology
evolving in the next decade?
I know we talked a little bitabout AI, but what else do you
see on the horizon there?
Dr. Jonathan Hill (18:49):
Yeah, well, AI is what I see and how I see
it applied.
is, you know, everybody now haselectronic health records.
We're getting to the point whereyou can get a genome sequenced
for less than a thousanddollars, which for clinical test
standards is quite cheap.
Um, that's the raw data cost.
Um, and you only need that oneonce cause that never changes.
(19:12):
We and others are trying to Uh,develop new diagnostic tests
that can inform these models,and I really see over the next
10 years the development of adiagnostic AI, a tool that
doctors can use that can lookinto all of these different
kinds of inputs in history ofthe patient and make a very
(19:33):
informed decision.
Um, diagnosis and you know, it'sinteresting if you think about
your doctor, often they startgetting really good at
diagnosing the things that theysee all the time, right?
And you know, you get the doctorthat in winter is like, Oh,
that's this because I've seen 20cases of this already.
I know it's going around RSV orwhatever, and I can recognize it
(19:56):
now, right?
So imagine a doctor that had theequivalent of 10, 000 years of
clinical experience and amillion patients.
Right?
What kind of diagnoses couldthey make?
Maybe that aren't so common, buta rare, but once you start
thinking about those numbers.
Become enough that you can seethose patterns.
That's what diagnostic AI canbecome.
(20:16):
We can have a million recordsfrom different people of all
different walks of life, buthave all the data there train
models that can even recognizerare disease.
And really help doctors out.
I think doctors will still havea role.
We're not going to, they're notgoing to get replaced yet.
That might be 50 years orsomething, but 10 years, they're
still here.
(20:37):
Because that connection with thepatient, the ability to explain
things to the patient, theability to have some intuition
on whether a particular patientwill follow a treatment plan
well, or, you know, family andsocial factors might be involved
there are still important.
And we, as humans have greatintuition.
They're right, but the diagnosisitself is better suited for a
(21:01):
computer than a human.
You're taking lots of datainputs, finding complex patterns
in those and then combining themto get.
That also means that over thenext 10 years, I hope to see a
shift in my field in thediagnostic space, looking more
at our diagnostics, not just asan answer, right?
(21:23):
Right now, when you get adiagnostic, you want the bright
blue band to show up.
Um, as you have COVID, forexample, right?
You want it to be the answer.
And that's quite limiting.
But if we look at it as this isa data point that will be
combined with a whole bunch ofother things like their genome
sequence, et cetera, or imagingresults or those kinds of
(21:46):
things, and think of it not asone single standalone answer,
but one part of a big puzzlethat will make our, our
diagnostic work much morepowerful.
Meg Sinclair (21:59):
How are you preparing your students for, for
the future decades to come in,in the industry?
Dr. Jonathan Hill (22:06):
Well, likely, I think.
Yeah, I think that's a big partof it.
We talked about teaching themdata analysis skills, right?
Trying to think that way.
We don't think in biology oftenin a big data kind of way, just
the way we frame our questions,things like that.
Trying to get the students tostart seeing things from that
different paradigm.
But the biggest thing that I tryto give them in all my classes,
(22:28):
Is the tools to learn on theirown after they leave the class,
because, you know, we can sithere and say, Oh, 10 years.
I think it's going to look likethis, but we don't really know,
uh, where it's going to go.
It what we do know is it's goingto advance and change a lot,
right?
And if they're thinking thatthey're done learning everything
when they finish medical school,for example, they're completely
(22:51):
wrong because the field is goingto change several times
throughout their career, andthey have to have the skill set
to pick those up.
And learn as they go to, to makeeffective use of these new tools
as they come on.
Meg Sinclair (23:05):
Yeah, I think that's good advice for students
in any field, um, in our everchanging landscape, um, with
technology.
What role do collaborations andpartnerships play in your
research and developmentefforts?
And can you share any excitingprojects you currently have
underway?
Dr. Jonathan Hill (23:22):
Yeah, I can.
So, uh, collaborations are sohuge for us.
First of all.
Uh, there's kind of twoscientific leads in our company,
me and Tim Jenkins were bothprofessors at BYU and this
entire company came out ofcollaborations that we have.
And we always talk about thesynergy of our skill sets, how
(23:43):
there are things that we've beenable to do that I never would
have thought of, but I had theskill set to answer and just
didn't realize it.
Um, and things that he had theskill set to do.
Um, but I didn't know how toanswer the question.
Right.
And so, uh, by working togetherwithin a team, we can get those.
And of course, with any companythat expands out, um, I like to
think I'm pretty good atdeveloping these techniques,
(24:05):
working on the bioinformaticsand the bench, those kinds of
things.
I have no clue how to run acompany.
I don't know how to do series afunding.
And they throw out all theseterms that I don't even know
what they are.
Right.
There's a whole nother sidethere that you have to do.
Thanks.
And then within our company,we've realized that we've built
this platform for measuring thechemical modifications of the
(24:27):
DNA in a diagnostic space.
But, you know, we're not expertsin all of these diseases.
And so again, we have blindspots.
We won't even see the potentialapplications of this in many
cases, but others will.
And so right now we have severalcustomers who really are
companies.
Who have been working on acertain diagnostic or in a
(24:49):
certain space and say, look atour technology and say, Hey,
that might solve a problem thatI have.
And then we work together on it,right?
And they provide the intuitionfor the specific application.
We know the technology insideand out.
And together we can developthat.
So we're always looking for newpartners in that kind of way.
Um, our first clinical kind ofapproval.
(25:11):
Application are clear.
Application is going in rightnow.
That was done with a partnercompany working in the fertility
space.
Uh, and in this case, they'vedeveloped a test that will tell
you if a certain fertilitytreatment will work or not.
And so that's great for people.
These are often expensiveinsurance often doesn't cover
him.
So to know ahead of time if thishas a chance.
Uh, is a good thing.
(25:33):
Um, they have a couple othertests that they're developing as
well, related to fertility andprostate cancer, things like
that, we have another companythat we've partnered with, uh,
that is working on theAlzheimer's diagnostic, the
neurodegeneration right now,we're trying to expand it out
from just Alzheimer's toAlzheimer's Parkinson's ALS that
(25:53):
work is ongoing, but lookingvery promising, uh, we have
another one that's looking at itfor looking at Lyme's disease.
Seeing if we can diagnose thathard to diagnose disease much
better with our technology.
And then finally we have a neatone because the company started
from a student in one of ourlabs here.
Um, and she realized that shecould use this technology for a
(26:17):
woman's health typeapplications.
Uh, and so she's taken andcreated her own company and is
developing, uh, using ourtechnology, her own set of
tests, all related to women'shealth.
And applying it there.
And so we like to help peopleget going.
We like to help them solve theirproblems.
Uh, and we see so many newapplications that can come with
(26:40):
new partnerships in the future.
Meg Sinclair (26:43):
Sounds like a lot of exciting partnerships.
I can't wait to see who else youpartner with in the near future.
Dr. Jonathan Hill (26:49):
It's fun.
We don't like working alone.
That's boring.
Meg Sinclair (26:53):
That's great.
The more the merrier.
Dr. Jonathan Hill (26:56):
Yeah, exactly.
Meg Sinclair (26:58):
Um, and our last question to finish us off is
more of a fun one.
We love to ask each of ourguests if we ran into at the
bookstore or at the BYU libraryin which section would we find
you?
Dr. Jonathan Hill (27:10):
Oh, me personally?
Meg Sinclair (27:12):
I will
Dr. Jonathan Hill (27:12):
admit most of my reading right now is
scientific papers.
The technology is moving so fastthat I just have to keep up with
what's going on.
Right.
Uh, but I do enjoy kind ofclassical fantasy kind of stuff,
Lord of the Rings fan.
Uh, so you'd probably find me inthat section if it's a day where
I feel like I'm actually caughtup for once on my scientific
(27:35):
reading.
Meg Sinclair (27:36):
Or needed to escape from all the
bioinformatics
Dr. Jonathan Hill (27:40):
need a break.
Yep.
Meg Sinclair (27:43):
Lovely.
Well, thank you so much forjoining us.
Dr.
Hill.
Where can those go?
Who wants to follow along withyour journey and find out more
and connect with you?
Dr. Jonathan Hill (27:51):
You can find me on LinkedIn.
I've got a profile there.
And then also you can find ourcompany and what we're doing at
wasatchbiolabs.
com.
Meg Sinclair (27:59):
Great.
We'll get those posted in theshow notes for our listener.
It was great having you today.
Dr.
Hill.
Thank you so much.
Dr. Jonathan Hill (28:06):
Thanks for having me.
Thank you for listening to thisweek's episode of From Lab to
Launch, brought to you byQualio.
If you like what you've heard,please subscribe and give the
(28:27):
show a positive review.
It really helps us out.
For more information aboutQualio, our guest today, or to
be a guest on a future episode,please refer to the show notes.
Until next time.
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