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Welcome to Flow Stars, candid conversations between Dr.
Peter O'Toole and the big hitters of Flow Cytometry.
Brought to you by Beckman Coulter at Bite-size bio.
In the latest episode of Flow Stars, I'm joined by Ryan Brigman,
professor Emeritus at the British Columbia Cancer Agency.
Out in its wide ranging conversations,
we discussed the clinical impact of bioinformatics and how to get data
without a wet lab. If
I, if I'm gonna do bioinformatics is my career. Um, I'm gonna want data,
well, standardized. I want lots of data 'cause I'm not gonna have a wet lab. I,
so I'm gonna be reliant on others for data.
I've kind of got this idea back from my PhD that you can get a lot of data from
other people. They're a really, really cool thing.
The clinical impact of bioinformatic data.
But also, I had twice as much data as you'd ever had before.
And one of the things that we, that was interesting was in this disease, we can,
we know that this size of the c repeat is associated with your age of onset.
And so, so the, the protein expands this, the CJ expansion.
If you have over 36, um, repeats,
you're gonna get Huntington's disease some time of your life.
And what really motivates academics,
What we do, we do it out of love for what we do.
And that's a very different motivation.
And I don't know that that's something you can teach.
That's something you have to feel
All in this episode of Flow Stars.
Hi, I'm Peter Oto and welcome to Flow Stars.
Today I'm joined by Ryan Brinkman from the British Columbia Counter Agency or,
or was out there. Ryan, how are you today?
I'm doing fantastic. Thank you so much for having me here.
So, correct me if I'm wrong,
you just said you are Professor emeritus and you are retired
Semi-retired. Um, I, but, but by now my, as of last night,
my lab is the hugest it's ever been.
I just hired five people into my academic lab since January.
On top of the other five or six I had,
it just doubled my size after I left. Um, wow.
Is that all on your salary that you used to have? That has now gone to,
we've employed five new people.
Well, one of, one of it is I, I left with a bunch of cash in the bank. Um,
and the two have some really cool projects I'm trying to finish off. And three,
um, there is no three
God, I I, I'm gonna ask a personal question though.
You said no question was off Target. So he say, well, yeah. Do you,
I, um, today I'm 56. Yesterday I was 65. I just had my birthday.
Wow. Well, happy birthday.
Thank you. Belated,
A bit late.
I, I was gonna say, I, I'm in the uk but that's ahead of you anyway.
Not behind you. I could have got away with it, technically
retired at 50 55.
Retired, uh, retired, but then I started my, then I, I just started my career.
So January 1st, I, um, I joined Matics.
Um, and it's the best job I've ever had, could ever imagine to be, um,
doing essentially the same job I had up until January 31st.
So now I am a VP and research director of, uh,
at dot matics, um, doing flow geometry bioinformatics there.
Wow. Now, now, now. So this is gonna be interesting. We'll come,
we'll come back. Let's stay where we are and we'll come back in history.
So your VP of dogmatics to, to,
for those who don't know what.dot Matics is very briefly go on,
give us a sentence on what Dogmatics does.
Um, do I, should I should have this down. Um, you've heard of,
um, let me, the story is, it's, uh,
a company that is helping large organ, mostly LA very large organizations.
Farmers are primary targets, do science better. Um, and so like all tech,
like everything today, uh, data is a massive problem. Uh,
and it's being generated more and more and more. Lots of variety of data,
the volume of data, um, software is a great solution for that.
AI is a great solution for that. Um, Matics is a platform of companies, uh,
that bring together scientific solutions that enable other research. So, uh,
case in point, you may have heard of Prism, um,
previously known as GraphPad Prism. I use that in my PhD. They, they,
that's part of the portfolio. Um, Matic is the electronic NA notebook, uh,
part of that portfolio. Um, they have Austin Tech, um,
three companies in flow cytometry, um, FCS Express, omic, um,
my previous company, side Effects is part of that that you no longer see that,
that got subsumed into Omic. Um, they have a bunch of tools in the,
uh, protein field, uh, clinical trials. Just,
it's a whole, it's essentially, you can think of it as, um,
the Adobe of Scientific software.
Okay. They they changed their name, what, two years ago-ish, or,
Yeah. So it was, it was Insightful Science was the umbrella organization, um,
that started it. And then they acquired Dot Maddox. Um,
and because of the name brand recognition on Matics, um,
that is a large part of it. Um,
'cause nobody heard it and nobody essentially knows what, what is it, you know,
ENCY Science, but a lot of people heard of Matics. So, um,
that is for the umbrella. It's both the, uh,
the platform and the umbrella name for the whole organization.
So you retired from academia, but now
With a pension.
Uh, but, and then, but taking up a new career, therefore, yes. Uh, in,
in, in the industrial side. Yeah.
And, and I wouldn't say it, it's a, it was a new career. It's a,
it's a new way of doing what I've been doing my
whole life. But at scale.
So let, let's take you back then. I, I, so obviously, uh,
what would be the best term? A flow cytometry. Bioinformatician
It would be a,
That, that, those are three very good words to describe my academic career.
So where did all this start out? Where did you get into?
I place cytometry. Yes. Bioinformatics. Yes.
It's not doing place cytometry, bioinformatics now.
That's getting very niche and specialized and an area that is exploding at the
moment. So we need
More of it. It's not something that I grew up wanting to be.
So, so that, that all started, um, when I was, uh,
when I was let go from my pre position immediately previous to this one, um,
I had to find a new one. And I was inter I wanted to, had to stay in Vancouver.
'cause Vancouver beautiful. Um,
and I had two, two places I interviewed at. One was, uh,
BC Cancer and the other one was a Genome Sciences center in Genome Science
Center. I would've grown up to be, um,
somebody who looks at DNA sequence data, uh, you know, um,
sequence bio mathematician. Um, and you know,
and very familiar with that field. I worked at, um, Washington University, uh,
working in the genome space,
like thousands of bio Ians were doing it at the time. And then I interviewed at,
at BC Cancer. And they tour you around and introduced the other,
the other PIs there. And Clay Smith was a, was an md uh,
at the time. And, uh,
he's turning me around the lab and there's a bunch of computers out there,
and they're all doing flow cytometry data analysis. Never heard of it.
And he says this, we do this, we do this dating thing,
you might wanna look into that. And, and I looked,
'cause it's very hard and it's, it's subjective and time consuming.
And this was back in 2002, I think. Yeah, 2003. So that,
that night I went home and I never heard of this technology. And I,
and I went to Google and I, and I googled post cytometry bioinformatics,
what is, what is this thing that I could do?
And I literally got about four hits, uh, at that time.
And I went to PubMed and I think there were something like eight papers that had
been published in 2002.
And I looked at the papers and they were not bioformatics papers when,
when you put the Full Sergeant Bioformatics. And I thought, okay,
I can go be the guy, another guy doing sequence analysis,
but there's nobody doing this thing. I, I, I knew,
I knew I loved bioinformatics. I knew I love big data, um,
computers, technology algorithms. Not that I'm good at any of it,
but just what I love being involved with. I thought if I'm gonna do something,
uh, I'm gonna do something when nobody else is doing it.
'cause I'm not that smart. And, um, this is like a,
it's a blue ocean kind of experience. And then I got super lucky. So then,
um, the first grant I ever wrote was a, was a travel grant,
which isn't really a grant. It was like, write a paragraph. Um,
and it got me $5,000 to go to isac.
It was in Monier. And, um, all,
all the good,
everything good in my academic career is come as a result of isac and as a
subset of CY and a subset of that cyto,
everything that's been fantastic in my life has been through isac.
If you're in flow cytometry,
if you're listening to this podcast and you're not a member of ISAC and you have
MENAL meeting, you have missed out on a large part of your life, uh,
or potential for greatness in your career. And so, um, I went there, um,
and I idea if I, if I'm gonna do bioformatics is my career. Um,
I'm gonna want data, well, standardized.
I want lots of data because I'm not gonna have a wet lab. Um,
so I'm gonna be reliant on others for data.
I kind of got this idea back from my PhD that you can get a lot of data from
other people do really, really cool things. Um, and so I I I,
I went there with idea. I, I, I can do this my whole life.
I can do something around data standards. So that's gonna enable everything.
And so I, I wrote to, um, Bob Murphy and, uh,
the head of the data standards group at the time, so I can't remember his name,
um, said, I don't know anything about this field. I'm a complete newbie,
but I'm really interested in super keen, and would you guys meet with me?
And these are like two, uh, Bob Murphy is an luminary in the field. He,
he was a, he's a head of isac. He, you know, groundbreaking research.
He wrote the first paper on the first real paper on clustering
of post geometry data. Like, um,
when it was back when there was three colored data. And,
and in his paper he says something really cool, you know, um,
now that we have five colored data,
things are getting really complicated and we're gonna need new ways to analyze
data. It's literally in this paper. It's like,
you look at that and you laugh now. And, but they sat down with me.
And this is the wonderful thing about cyto, is you can walk up to anybody.
Everyone's so welcome. I, I,
I guess I have very limited experience with scientific societies. Um,
ISAC is wonderful. And they sat, they sat,
I have this beautiful memory of the two of them sitting with me. Um,
uh, and they just wanted to hear what I can do.
And they took the time when I knew nothing in the field and like, yeah,
this sounds good, we'll support you. Um, and it kind of took off. And then,
so then I wrote my first, my first grant was an NHR one. And I had, um,
a wonderful experience, um, that I had four months of nothing.
I was left to my own devices at the BC cancer to find my own way for better or
worse. And so I had four months to do nothing but write my first grant,
the best grant I ever wrote. Um,
and so I wrote to the four people that I found on Google and saying,
I want to do full scar mathematics. And they all said yes, which was great. Um,
one of 'em was Adam Trier. Yep. Who was the head of,
of Bojo, uh, Perry Halland, who was at BD Biosciences.
You may have heard of them. Um, um,
Michael Oaks. Um, yeah, I found him,
I can't remember anything about what happened with your part of the grant.
And then the guy who I wrote to, um,
he bad thing is that he left after he got the grant. And so I was like,
oh my God, the guy who was gonna be my grant left. Best thing that happened,
Robert Gentleman took his place.
Robert Gentleman invented R and Buyer Conductor. All of a sudden, I,
Robert gentleman on my RRR one to do flow cytometry for bioinformatics.
The, the way things happened could not have been any better. So I had the,
the rock, the,
the man who invented r the A guy at BD Biosciences and the CEO
of Flo Joe on my first AHR one grant. And I knew nothing about flow cytometry.
So I, I was off to a good start. That's,
that's how I started my life in Flow cytometry.
Uh, I love it. I also like the irony that, you know, you've got, so, you know,
thanks to FlowJo and here you are now working for FCS Express, kind of,
they're big rivals in, uh, yeah,
Yeah. But, but, but, but we're, I, I'm collaborating with people who,
who are direct competitors. Like even today, um, uh,
competitors on paper. Um, so we're doing, we're doing this project on, um,
so we're doing a workshop, uh,
on AI and flow and ML and flow cytometry for clinical diagnosis.
I'm there, um, Sheri Green from Ozette is there, uh,
Camila, who I love dearly also a competitor of Matics is there.
We all want the same thing. We all, and, and, and when, when I work with pharma,
all the pharma want the same thing. Um, so I,
I have really close collaborations with BMS.
They're really happy to work with other pharma to make the world a better place.
And, and, and again, my,
my whole stick is collaborative science and we can compete, but,
but that doesn't mean we can't collaborate.
Yeah. I, I, I am thinking, you know, we,
we run hands-on flow courses and we have the different companies all come and
support it together. And, and, and they are together. They talk together,
lunch together. 'cause they're teaching together.
They're not teaching against each other.
They are collaborating for the teaching side. It's a big,
and you mentioned teaching and I,
I only got a few photos and you got a montage of,
I dunno where to go if you, if you watch it, if you're listening to this,
you'll have no idea what this picture is. But it's a picture of, well,
1, 2, 3, 4 different groups, uh,
all presumably of different collaborative projects
Is all the same collaborative projects. So this, this is all,
this is all the flow cap meetings that we have. So Flow cap, um,
was a series of, um, competitions, for lack of a better word, um,
is more about understanding a series of meetings to understand the
state-of-the-art of automated analysis as opposed to telemetry data. Um,
so Nima, who, uh, was the PhD in my group,
who is now a eminent scientist at Stanford, um, led led, um,
was the first author on a series of those, um,
on a couple of those publications. One of those publications for sure. Um,
where we're trying to understand the state of the art.
And so we brought everybody together in the field, which is literally,
that's that wa that is all of flow cytometry at the time.
I think we did a really good job at re reaching out and bringing people together
as part of that work. Uh, again, it was funded, um, by,
by bd Jack Dunn, I Love You. Um,
he bought pizza when there was 10 of us in the room of people who are doing full
cytometry and, and just trying to get the field started. Again.
A lot of the early work was just, um,
trying to get the software and r developed. Um,
but it's all just everyone want comes together just to bring the field
Oh. Which is really important. I just,
again, just going back when you were 10 years old,
what did you want to be?
Oh, older, I guess. I, I didn't have, I didn't,
I didn't really have like, I want to be an astronaut thing
in, in my head. Um, I, I didn't really,
I didn't ever really know what I wanted to do. Even when I was in university,
I didn't really have, I think I changed my major three times during my, my p
So what did you choose to start with then?
Um, bio biotech. Um, I always loved computers. So when,
when I was a kid, I had a, a Vic 20, um, that,
that's when he had to put a little cassette. My, my brother worked at, um, uh,
digital. They no longer ex exist anymore. So when,
when I went to summer holidays there, we, we, I could dial into their,
their ax 11. And that's when you dialed in this,
this is when I started computing.
When you dialed in with the phone and you listened for that, like in matrix,
I was living that life.
And then you hear itch and you push it into these plungers to connect. And then,
so I could play Star Trek, um, like little Xs,
little Green Xs on the screen and move them around. Actually, before that, I, I,
my first programming was on, on punch cards. I did programming on Fortran, uh,
in high school. So I always loved programming. Um, so I knew that was gonna be,
I never took any formal training other than in high school on that.
Um, but I always knew that was gonna be a part of my life.
But I also loved science. I love discovery. Uh, I love I,
the thing I like most about my job is being the first to know something.
Like nobody else knows this. I have like, my,
my Eureka moment and my PhD is like, this is super freaking cool. And, and,
and I've solved a problem that people have been thinking about. It's the best.
It's like a total head rush. Um, the same kind of head rush you get,
you get when you're on stage. It's just shivers. So I, I, I loved science, um,
but I didn't know what I wanted do. So I did bio biotechnology.
Long answer or short question. Um, it changed over a few times.
And it wasn't until third year, I think George Car. Um,
it was a genetics course. And then, um, it was the, the, the,
the magic dance of,
of polymerase and DNA and how that all just
works blew my mind. Like,
how incredible is this dance of things that makes
life exist that it's so beautiful? Um,
and it's like, this is, this is science is a thing. I, I don't know.
I I don't know what I was gonna do in science, but I just knew that was,
was my thing. Um, still like computing. Um,
and I guess my big break was after, you know, I did some database. I had my,
after my undergrad, I did some work in, uh, doing some database stuff.
Um, but then they, they did a post post for a job at Wash U in Seattle. And, um,
a friend at the time, um, ended up marrying, uh,
Fiona said, oh, here's this cool job in, in, um,
wash U that looking for buying petition.
And the borrow was super low at this time to get into buying Matics. Like I,
I knew how to run SQL Query in a, like, it was basically access.
It wasn't even sql. Um, but I loved computers and that was enough to get me in.
And then I learned how to do, uh, pearl programming and stuff. And then I,
and then what happened is, I, I was in an academic environment and, um,
it was in St. Louis. So two things happened. Um, what, um,
ended up, uh, through, through a series of things, um,
was gonna start a family. Um, it wasn't gonna be in St. Louis. Um,
wanted to go back to Canada for various um, reasons.
Um, lifestyle being one of them. Uh, and I,
and I was surrounded by academics and,
and as somebody with just a undergraduate degree, all the cool people,
all the cool projects were going to people with PhDs,
like they're doing really amazing. Cool, I want to do that. But in, at least,
at least in where I was, and, and for good reason, um, you,
you kinda needed a, a card to say, you're good enough to do some cool projects.
And so I knew that, um,
I wasn't doing a PhD to get smarter or to learn more.
It was, it, to me,
it was just a calling card that would let me get to do cool s**t.
And that worked. Like I did my PhD on Huntington disease.
Nobody ever, ever have never touched Huntington Disease again.
It taught me what it, what it did teach me is how to, how to do things.
It didn't, it didn't educate me in a domain that was ever useful.
It taught me how to do science, um,
how to do it taught me how to do large collaborative projects. Um, so,
um, in my PhD, my, my whole reason I got my PhD is,
'cause my professor had two databases. Um, one was a research database.
It was on people who had Huntington's disease and he had his research database.
There was one access database, and he had a clinical database, which is,
I forget what it was in, I think it was also in access. Um, and never did the,
the two were not connected. Um, but his question is, you know, can can we look,
can we mine this data to discover something new? Was that, that was, he's like,
here, here's some data. Figure something out. Uh, and I thought, well, what,
what if I put these two databases together? Um, let's try that.
And so that was my PhD. Put these two databases together and put all the,
it wasn't that magical a thing to do.
But also I had twice as much data as you'd ever had before.
And one of the things that we, that was interesting was in this disease,
we can pre,
we know that the size of the CHG repeat is associated with your age of onset.
And so, so the, the protein expands this, the CJ expansion.
And if you have over 36, um, repeats,
you're gonna get Huntington disease at some time of your life story. Um,
these repeats can go up to 121 and we can make this diagnosis at any time.
So devastating disease. Um, and so it, it, there's a whole,
did a few papers on, uh, the clinical consequences of this,
this diagnosis that people can have. It's really interesting stories. Like, um,
uh, there's one person who had lived their life, their,
their parents have it. So you have a 50 50 chance of having Huntington disease,
and they never wanna get the genetic test.
And so they lived their life freely, um,
without purpose, if you will, because they had this shadow over there,
this looming Yeah. Horrible thing.
And then they got genetic test and they found they were gonna be okay,
significant psychological trauma,
which is the opposite of what you think you have.
All of a sudden you're gonna be okay if everything's fine.
And then they lived their life without purpose. And then they,
there's devastating. It's like, so anyway, so, so can we figure out this?
So the thing was, can we figure out the age of onset of, of the disease?
And so I had this, those large dataset. I published a paper. We, we had some,
we can get a pretty good idea when they have the age of onset. But then I,
I thought, okay, well, can we do better? And then,
and then there's a grad student you can get away with a lot.
So I wrote to everybody in the world who had an Huntington's disease lab and
said, can I please have your data? Everybody gave me their data. Wow.
Part of that was part of that.
And there was 40 labs around the world who had never heard of me.
They heard of my supervisor, maybe that helped. Um, but they said, here's our,
here's all our clinical data. Now,
it was easy for them to gimme this data because it was super de-identified.
The only thing I wanted was their size of CHP and the age of onset.
And now I had 10 times more data than anybody had ever thought of having in my
life. And then we can come up with this essentially perfect way to,
to give a prediction of the age of onset with like super tiny confidence
intervals. Like by age 38, by age 40,
you have a 98% plus or minus 2% of having age of
onset, which has profound implications for clinical work.
Because if you give a treatment to somebody, um,
you want to know how long have you delayed their onset.
But unless you have those kind of,
if unless you can make those predictions of when the onset could be,
you could have never, um, inferred when the onset would've happened.
So you don't know how well your treatment is really working. So that was like,
wow. So for me, that was the best thing.
So I could see how my work would impact clinical science and development of
drugs. And that has been my go-to thing since then is,
is using software to enable, um,
the development of drugs, of pharmaceuticals, of big data science.
And that's what I'm gonna do. That's been my whole shtick now in post geometry.
Bioinformatics is developing algorithms, big data, all that.
And with the fo with my own focus is enabling other people to use those tools to
make discoveries at scale. And,
and that is the amazing thing about being a documentation for me, is we can,
I can hand off my algorithm to somebody else at, um,
some large pharma company who's like, um, we're working with, I guess I can,
this will be, yeah, they'll be talking about it.
Cyto We're working with Kite Pharma. And so they're doing this,
this flow data analysis by hand. It's too time consuming. Subjective,
I'm just going on, stop me at any time here. Um, um, they're doing this,
they're doing this data analysis, trying to draw these circles around dots,
which Clay Smith said, can you please solve this problem?
Fast forward in my life to about three years ago, we have this problem solved.
Um, we hand it over to other people,
and we're enabling to save patients that they weren't willing to give
the drug to. Because when they're doing the own, um, CAR T therapies,
because they're not certain about how they're doing the man analysis,
and they're not willing to make that life death call that we're gonna give this
$300,000 drug to this individual because we're not sure how we're,
we're not confident of our gating, which is like, but then we, we,
we walk with them with our algorithm. We show how it's working. We,
we prioritize it over a couple of months. And they,
and because we can explain our algorithm to them because they get the ability to
tweak how it works, they're super confident how it works.
They look at a thousand patients,
they're super confident that it's giving the results. We, we, they,
they're able to make their call on three people that they weren't able to make
the call before we got everything else. Right.
Now they're rolling out in production across the company. Um,
and now we're saving lives at scale. And we have so many,
I have so many stories like this that that's why I am today.
So is that why you started your own company to enable that? That Yes,
that's exactly, oh, oh, that's a brilliant story.
I I did not start my company for that reason.
So when I started the company, uh,
and that was one of the best things I ever did in my life. Um,
because I always had this feeling if I was gonna do, if I,
if I had been something else to answer your question that you asked before, um,
and I didn't know this later, I was like, oh,
I will always wish I had done my own company. Um, I,
I didn't really care what that company was doing. Um, I just,
I like, I like coming up with ideas and having other people do the work.
That's not, that's like the perfect job. I'm a lazy scientist. Um,
and so I thought if I was, if I was ACEO,
I could come up with cool ideas to do stuff. Somebody else would carry it out.
I I can work on the ideas thing. And so I, when I, we had,
when we invented this algorithm that does automated gating flow density, um,
this is in my ca, academic lab gave it away for free. And I knew it was perfect.
It it is perfect. It, it always works. There's nothing,
we've not been able to analyze awesomely.
I'm not trying to sell you anything because we give it away for free. Um,
it needs prioritization. We can talk about some of the issues there.
So you have to prioritize it for every panel. That's the downside.
But when you do that, it's magic. Um,
and so I go give these academic talks.
And one of the best things about being an academic in full cytometry,
back in the day, there was not many very many of us.
I got invited to go everywhere.
'cause it's like something new that people wanna hear about.
So I've been all over the world to give these talks and people come up to me
after me and say, yeah, we'd love to use your algorithm,
but we're not bi petition. Can you help us? And it's like, yeah, but, um,
it takes, you gotta grease the wheels, right?
It takes money to do this parameterization. And I thought, you know, um,
and they always come to me after they've written the grant,
after they collected the data. It's like a, it's an afterthought.
And so they don't have any money, more money in the grant.
So it's kind of complicated. Um, but, um,
so I was doing that for a while. And then I gave a talk once and some, some,
some guy in the industry came over to me. So they had the money.
It was like a small little project,
but it ended up being really difficult to get money in an academic environment.
Um, because you need a contract Yeah.
That says we're gonna deliver you this thing. And,
um, I'm not the one signing off on that. It's the institution.
And so all of a sudden they're on the hook if Ryan Britman can't pull off this
analysis. And so I, I did that once.
It ended up being like months of work to get that contract through the new fault
of their own, but to go through them. And so, okay. Um,
so is there a way I can get around that? If I start a company,
then I can work with academics. I work industry kind of on the side,
these little groups, um, and get, get the, the paperwork outta the way.
I'll handle the paperwork and then subcontract.
I'll work to my lab to do the work, and that'll,
I'll help all these little academic groups and maybe some,
some these small things that are coming up, these small, this, like,
I had this one thing, this small guy was like two people in the company. Um,
like they, like they mailed me their laptop to installed the software on,
and I mailed them the laptop back. It was to do some five color data,
really simple stuff. So I made,
I made my little MySpace website with a blinking lights just so other people
would find it. Um, and I knew we could help pharma,
but it's like, it's like, I, I could help Genetech,
I could help Pfizer and BMS,
but I'm just a little professor guy and I don't even know, like,
I can't call anybody to say we work with us and
to, and it's like few tens of thousands of dollars.
It's not something that some person at the lab can just sign off on it.
It's like, it is never gonna happen. It's not, they're not my clients.
It's just these little people. I see that.
But then I started getting emails from pharma, you know, Google search worked,
I guess. And it's like, oh my God, I got, I'm, I'm, now I'm working with pharma.
They, they found my website. And, um,
it's not, and that, and so then three years late, after four years, so I,
three years, three years of this company running it kind of built up. We had,
by this time, we had six of the top 10 pharma as our clients doing work. And I,
and I've seen this working. You know, we, we helped Novartis, um, getting ca,
ria, it's like the first CAR T therapy.
We got a really nice thank you for helping us with our FDA submission.
And it was like, this is an incredible thing. Okay,
this company thing is working,
so maybe I should go back and learn out how to start a company.
I just did it totally on the fly. It was, it was, it is like a one man show. Um,
I was A-C-E-O-I was the bookkeeper. I did all I wrote,
wrote and reviewed all the contracts.
The only thing I didn't do is the accounting at the end of the year.
'cause the tax man is the one who's gonna bite your ass. And you end up like,
that's the one guy you gotta make happy. But I did everything else. Um, I, okay,
actually, I should figure out how to do this company thing. And, um, and I, I,
I never thought the company would Mel to something. When I started.
I made like one share in the company. Um, when I,
when Justin left, uh, my academic lab to go into Paraguay, we just, um,
started a big project with Pfizer. I thought, okay,
you can have this to get used. You can do this project when you go off to, uh,
Paraguay to get you started, and then you can find your life there. Um,
so I only had one employee at the time, and I thought, okay.
And Goldner had to do a company, and I went to UBC.
So if you're in university and you want to do start a company, go, go.
They have project at every university to do this. Stanford is great at this. Um,
UUBC was really good. They, they have a little project called, um,
entrepreneurship at U bbc. So I went to this course, um,
did like the intro course. I thought, okay, I, now I get, I get the basics.
Like, know your customers. Like, oh,
that would've been great if I could have figured that out and have your whole
project plan and do all this. So not, not now.
It's like the intermediate course and the day we're gonna start the intermediate
course. Um, I get this email from Insightful Science saying,
we're interested in your company. Do you want to buy it? I'm like,
are you kidding? There is no IP in the company that, um, there's one share.
Um, but yes. And so my, my first day at, at, at the, at the,
um, entrepreneurship is What can you tell me how to show sell my company?
It wasn't like how to get it. It wasn't, the exit strategy is like, I,
I didn't know I needed one, but now I have one. And, and that started my,
and that started my, uh, links with Ency Science. And that was about two years,
two, three years ago now. That was right at the beginning of covid. Um, the,
the lawyer company. This whole thing is like, stuff can still work during Covid.
Um, because the whole, I never actually got taken out to dinner.
I think when you sell your company, they should take you out to dinner.
They still owe me one. Oh, you still have, um, still haven't got my, Hey,
come on. Come on. You're a VP yourself out for dinner. Yes. Yes. Take myself.
Yourself out for dinner on work and say,
Yeah, yeah. Um, but that, that was an incredible,
just not my whole life has been what that just happened
without any planning.
So I'm gonna flip because you mentioned Covid and the impact of Covid.
And I have just a few pictures. I have this one here. So this is a, obviously
a, a cytometry parit paper. But this is about sars cof too. And, uh, this is,
uh, with Andrea and Sarah who are actually took previous podcast guests. I,
these are big cheeses.
I know Andrea was pretty fundamental in the covid
response and tracking within Italy on, on a national level. Yeah.
And of course Italy for you, it was, was what
It was when it was, when the whole world went. Oh my God. I mean,
that's when I think the world realized, like we're
Yeah. And, and it was right at the heart of the response.
'cause that's for Europe. That's where it kicked off. Well, it
Certainly where it was found or, or most prevalent. Yeah. Whether it's, yeah.
Let's let others argue about that. So this publication here,
so tell me more about this publication.
Publication, uh, that, that this is a beautiful story. Um,
so this is right at the beginning of Covid, um, right, right. During lock,
right when lockdown started, like this is when you drive around,
it was like the zombie movies. Like the streets were deserted in Vancouver,
you could drive down the street any anytime of day.
And there was nobody out in the road. It was like one of the neutron No,
not the neutron bombs. 'cause his car would still be left.
It's like everyone was gone. Yeah. Um,
and I, I got a,
I got a email from a colleague, um,
who I'd never worked with. But again, somebody knows, somebody who knows.
Somebody says, um,
can you do anything with flow cytometry and covid data,
like flow cytometry data and covid? Is there any link between those two?
I'm like, probably, but this, this is just at the start of covid. It is like,
well, there's no data on it. Um, but it's a disease of the immune system.
Probably there's Right, it's an infection.
So there's probably somebody somewhere has probably thought of putting some data
through a flow cytometer. So, yes. And um, he's like, well,
can you get some data? I'm like, well, I, I went to Google. Like there's no,
or PubMed or whatever. There's,
there's nothing that's been run through a machine yet,
but I could get some soon. Um, so okay, so can, can we do, can you,
can you come up with some idea that we can, um, uh,
do something with flow cytometry? I forget what the pitch was,
but something like,
can you do something with flow cytometry data and put some data in front of
people to help them to help you analyze
data? I'm like, no. Like, no,
because it's super complex, right? You have, you,
you have these panels and you have to know what populations to define,
and you have to draw all these gates around them and you have to train them on
t-cell and b-cell biology.
And you just can't throw that data in front of somebody and have them analyze
it. That's stupid. Um,
but, you know, but, but, but the pitch was, well, we can do this,
we can do this collaboration,
but we have this gaming company who is kind of gets citizen scientist
gamers to analyze data for you. And I'm like, oh, a video game that,
and so I'm a huge gamer. I'd heard of this. It's called Evon Online. Um,
it's a game I never played because I played Wow. World of Warcraft for years.
The same, the same premise. They, they hook you in with incremental rewards.
And the only way you can win the how you win at Eve is to stop playing. Um,
and it's like, I, I don't, but I know it, it's like, it's a massive, massive,
massively multiplayer online game.
I'm gonna just switch 'cause you also sent me Oh
Yes. This one here. So, so, so what we did is we, and, and it worked.
So now we took Andrea, we put 'em in the video game.
And for the last three years, it's still going on today.
We've had gamers around the world analyzing flow cytometry data for us 24
hours a day. I think, um, we got 360,000 gamers,
give or take hundreds of thousands of gamers. Um,
360,000 accounts. Uh,
hundreds of thousands of gamers have analyzed millions of flows.
Photometry plots for me, I have the biggest flow of photometry lab in the world.
So we solved that problem of having people who do nothing,
think about flow photometry data, analyzed flow photo data.
And I have two week,
or I have a month to pull it off.
'cause I'm giving a talk at Cyto saying it worked. It,
the jury is still out at this. So, you know, when you, when they,
when they invite you to give a, a talk at a conference, I'm sorry,
those people at, at, I'm sorry. People at Cyto who, future,
future people who seen my talk, you know,
when they invite you back in November saying, can you, do you want,
do you wanna give a talk at cyto? And you say, say yes.
Or when you write you your paper or your, your, your abstract and you,
you're not done yet, right? Um, often. And we're like, no.
How Lucy job abstract My abstract was, I, they asked me.
I didn't know what I wanted to talk about. I I, so actually, we just published,
uh, actually for a machine learning algorithm for, for, for imaging.
how am I gonna shoehorn this into a cyto talk that I'm giving? Yeah.
Yeah. So, so I knew I could talk about this and,
but I have to make it sound sexy. So like, oh,
we've solved the automated getting problem. That's, that's much from now.
It's like, so now we're, we're four weeks away. Stay tuned. Um, it's gonna work.
Um, is it gonna look super shiny like we solved the whole world in four weeks?
No, but it's gonna happen. Uh,
and it's gonna happen because we have so much freaking data, um,
and it's good data.
So it turns out you don't need to have know anything about flow storm data in
order to analyze it.
And the only reason that worked is because of my work with flow density and the
algorithm that we'd built in the company. All, all, all you need to, to do,
to understand flow star data is look at a single plot in isolation. And so the,
so my brain exploding moment is, so I had this other algorithm called flow type,
and it's the way we do biomarker discovery, uh,
leveraging our flow density algorithm.
So what we can do is we take every single population that you've gated and
combine it with every other population to basically look at tens of thousands of
populations. Then you separate your,
your patients into treated versus untreated sick versus healthy.
And we'll find those that are different. And then I, and I realized,
and we we're just going through all these five variants in all combinations.
And the gag doesn't know nothing about data or nothing about photometry.
He's just combining them. And, and a single by very applied.
You don't need to understand anything about the biology, um, to basically,
if you, if the goal is to just circle all the things of interest,
you can circle all the bits.
And once I had those two ideas in my head that came outta my algorithms and say,
we can just, we have, it's a Shakespeare problem.
We have infinite amount of monkeys or gamers. Um,
we're just gonna give them all the data and they're gonna write Shakespeare for
me, which is, and the only reason why I did this,
why I had to do this is because, and I'm gonna sit up for a
second. As, as a society, as science, as post geometry scientists,
as a society we're horrible scientists. Um,
and this has been one of the things that I've been working on a lot because
unlike most every other field out there where there's big science,
there is no data sharing. Um,
there is a bit with, um, in flow repository and import,
but not at the scale that you see for other technologies. Um,
like every other, every other, if you go to any other journal,
if you're publishing a paper that has DNA sequence,
you must submit your sequence to one of the repositories. Yeah.
the only journal in the world that says that is optometry a for flow cytometry.
And so the only re the only reason why I had to do this project is if I wanted
to develop a machine learning algorithm to gate flows down your data,
I could not do that because nobody, we can,
some people share their FCS files. Sure. They put 'em on flow repository.
Nobody shares how they analyze that data. They might see one example,
one example PDFs, a couple examples in AP in APDF of a journal article.
Here's how I drew my gates around one sample.
That's not gonna get anybody a machine learning algorithm. Um,
and so data sharing enable science,
I, I've gotta say with Eve, uh,
you are not the first to be using Eve uhs on the Microscopists.
The other podcast, Emma Lundberg, uh, has her own,
and she has a super cool avatar and Andrea has
his super cool avatar. Do you not have an avatar on it?
They asked me. I'm, I'm not that. And I said, no. Oh, really?
'cause Andrea, I know. Um, I said, no. Um,
Andrea's a rockstar. Yeah, because,
and so he's there because we got our first data set, um, for, and,
and so when this project started, it's like, and, and they,
it was understandably that everybody wanted Covid data. And so he gave us, um,
the first samples that came out of Italy, um, with Covid data.
And I promised him, we analyze it all for, we had,
we had this dream that we're gonna analyze it in real time and give the data
back to the scientists. It didn't work out. Um, but we have,
we're gonna have something better is we're gonna have this machine learning
algorithm. We're still gonna do that. Um, but in the meantime, we,
we even able to take all the data and we develop a machine learning algorithm,
uh, that's gonna work. You know, um, really
Awesome. For, for anyone listening, watching or, or listening. You know,
we've heard about the importance of data science around Parkinson's and how
that's s and so,
and then we also had, uh, you, you've,
you've talked about the analysis going to Kite and working
with the pharma and Novartis, and now Covid
the diversity of this type of research area. And it's not a,
uh, you've made it equitable.
Fortuitously maybe made it equitable for fun. Not,
not because you want to make it. What gives you more,
IIII think I'm go, I, I think I know what your answer's gonna be.
What's the biggest motivation that makes you most proud?
Is it the financial gain side of it?
Oh, no. So I would've taken a, I would've taken, I would've taken a job at,
at Madox for like, it's not not a thing. This, it's, it, it's,
it's being able to do things at scale, at, at, at enabling,
enabling discoveries at scale is like, I, I, I, I, um,
I was in a relationship with a cardiologist once and, uh, for a long time and,
you know, saving people's lives every day. Like people are gonna die,
goes into save 'em. Great. I like fantastic. I, I love,
loved what that meant and meant a lot to them as well. Um,
but for me, the ability to, like,
I can save like hundreds and thousands of patients, develop new drugs,
enable new drugs that are saved thousands of lives, all,
all through algorithms and science is an incredible rush. It, it,
it gives me purpose to get up.
It sounds corny.
No, no, no. Not at all. I,
I think this is really important because I think there, there,
there's always a fear that the Best Buy mathematicians will go into banking and
the finance world, because that's where big money is. Yep. But I think people,
I, I think, what's the best way to word this? I, I don't, yes.
Some people will be financially motivated and that's not wrong.
And and they'll enjoy the thrill of the ride of that type of industry. Yeah.
Yeah. But people in academia are not there for the money.
They've got the same talents, the same skills,
but are there to make a fundamental. Hundred
And you mentioned earlier the rush.
I've seen something for the first time realizing something within the living
world that no one else has seen and perceived before.
It's like finding a new star in the galaxy. Yeah. You have that on the bench.
You have that on your, on your desktop where you find that result. That's new.
That rush is huge. And oh, to get data scientists,
bio mathematicians to see that it's not all about, don't,
don't just be attracted to the markets, you know,
biosciences your new medicines. It's where it's gonna come from. The,
without the informatics, we're not going to get the best drugs. The, the,
The, the people who work in my,
in my academic lab and also at Dox couldn't agree more. Um, they,
they tell me that they, they,
they fully well understood when they joined my academic lab that they could work
figuring out insurance,
it's a big data problem or figure out how to get more people to buy my widget.
That's a big problem. They are the people who I work with and,
and that dot Maddox, um, are motivated by our,
our ability to enable science.
Do you know what, if any, we could teach at the undergraduate level? So, well,
the relevance that the different impacts that people,
and I obviously we surround ourselves by people who are minded that way,
but I think far more, or don't,
they don't even realize there's potential to do that. No.
Be wonderful. And, and it's not, it's not an easy thing to describe.
It's easy to show on TikTok. I'm Flash, you know, look at my cars. Money, money,
money, happiness Association. Right? It's, it's, it's, it's,
it's a very easy thing to understand. Um,
but I think what the, what we do,
we do it out of love for what we do. And that's a very different motivation.
And I don't know that that's something you can teach.
That's something you have to feel. I I, I don't, I don't know that money
works that same, in that same way to motivate, yeah.
Different parts of brain.
That's good. I'm, I'm gonna change tack.
I'm gonna ask you some quick fire questions.
What's your, I No, come on. We,
I'm gonna start with the flow cytometry quick fire. What's your favorite? Okay.
Favorite color? Yeah. Um, I like blue. Blue and orange. They compliment.
So actually I, I'd say orange because my, my heritage is Dutch, so, or Ranya.
Um, thank you. Uh, uh, the Dutch natural color. Um,
I, but my, uh, don't, don't get to wear a lot of orange. So I'm wearing clothes.
It's blue. If I'm applying the flag, it's orange.
I, I'm gonna throw you a challenge to my, to make sure that, uh,
cyto in your plenary you are, you are wearing an orange top. Yeah. And, and,
and, and as a done deal for my plenary, I'm gonna wear blue.
I think I'm gonna get the better deal outta that. Okay.
Are you an early bird or night owl?
Uh, early bird or night owl? Um, is it a pow day?
If it's the pow day, I'm up at five o'clock in the morning.
'cause my alarm is going off because I'm, I'm checking.
My alarm is going off at the night. My brain alarm is like,
check this snow state. So one of the best things about my job is, um,
when I start, best thing about my life actually was learning that I, I,
I like snowboarding and mountain biking, uh, coming to BC and, um,
um, work life balance is important. And, um,
so when I started at dot Maddox, it's a global company,
and I talked to Brett and said, um, I'm, I,
I wanna be on New Zealand time and,
and I'm a better employee for working on New Zealand time. So that would,
that would mean I'm starting my workday late in the afternoon, right? But no,
my workday start,
my workday starts at eight o'clock because I'm at first in line to get on the,
literally the first in line at Whistler, um, to go,
go up and do some power runs. I do about two hours.
Like I wake up at five 'cause I'm so excited to get all my stuff on,
have some breakfast, get out, get out the mountain. I'm on in the lift line.
It takes about 10 minutes to get the lift line. I'm snowboarding,
uploading at quarter after eight, uh, do a couple runs.
I get to work and I am jazzed. I am so like, I'm pumped full adrenaline.
Uh, I'm excited. I I I'm not getting to work bleary-eyed.
I've just done like two hours of powder or just hurdling down some double black
diamond. And now I'm at work. Best thing about my job, but if I'm working now,
I'm, but I'm working till I,
I'll stay up till like 3:00 AM My best work is between like 10 o'clock and three
o'clock at night. Um, because emails aren't coming in, I can focus. So I'm,
I'm at both ends. I'm, I'm the spectrum.
Okay, so I, I I've gotta ask you really quick. When you get injured,
how frustrated do you numb?
It kills me. So I two, I love mountain biking, so I have a plate here.
My shoulder, uh, snowboarding knees, I have, I've racked my knees,
um, playing, uh, soccer. I've had ACL surgery on both. It's hard.
Like, um, there, there is a dependency,
biochemical dependency that has built up in my head from adrenaline
rush. And it's either from extra or, or,
or that rush you get after doing physical exercise. Yep. And, um,
when I'm injured, I am a s**t to be around because I'm, I'm,
I don't have that tick, tick, tick, tick, tick, tick, tick.
Or like doing weights, like hitting the gym. And when that, when one of,
when I can't do any of those. It's horrible.
It is one of the downsides. I guess
It's the downside.
PC or Mac,
Um, false choice.
It should be like A-K-D-E-K-D-E or Nome. What about Linux?
Pc, Mac, Linux?
Yeah, I, oh, um, unfortunately Linux, yeah, I,
I would, I would use Linux all the time. But then you have, so,
but then you have to tap out to do all the like, office kind of stuff. Um,
so it ends up being Mac. Um, but if I, if I could,
I'd be running my home distro that I rolled.
So if it's Mac, McDonald's, or Burger King,
Oh, then that's a, that's a false choice. That is for sure a false choice.
So there, there's, there's a, on the way to Whistler,
it's like a two hour drive. There's a McDonald's there and it's like, it's like,
ah. And so we had to like, you can't,
you can't do that to yourself all the time. So, so it's, it's pokey.
So, so you do do McDonald's Sometimes
I do. It's a Big Mac and the fries are so good. And a Coke Zero,
which I don't know if you can get there. It's, it's better than the Y Coke.
No, it's the big breakfast. It's a, it's a breakfast long breakfast.
If you get up early and you're traveling early doors drop off at breakfast and
get your caffeine kicked there. Talk about that coffee or tea.
that would've been a false choice up until I don't never drink coffee in my
life. Can't get past the taste or smell of it. Um, never got a hang for tea.
But then I discovered like a couple months ago really just, uh, London Fog,
which was invented in Vancouver. So you may not have heard of it, it, it, um,
that's my new, it's a, um,
Earl Gray tea about halfway and then lots of foamy milk on top. Ooh,
it's, it's, I, because I, I'm a, I'm a milk drinker.
So that it's a good taste. Lemme
His son drinks copious amount of milk and he writes, he operates, he's,
he's got his own operating system that he works in as well. He's,
he's also bioinformatic, well informatics. Uh,
easier question. Maybe this is one that I need to know for cyto. Beer or wine?
Or white? Red. Red. Bordeaux blends. Purvey.
I'll never forget you taking me to some beautiful bordeaux wines.
Purvey Luce. Oh
No. I, I, I'm, I'm a Californians infant Dell person.
Oh, well, okay. But the, um,
but I am blessed to live in British Columbia because we have a little
microclimate out in Kelowna over the mountains.
It's a desert like cacti grow.
We make some phenomenal reds in British Columbia.
Like I would put them next to some California ones, like a just top shelf. Um,
like they, um, very, very rich man. Very rich. Owns like four,
five or six wineries out there. Um, I forget his name. He's,
he's trying to make the world's best pinot noir.
we're gonna have to move on because I'm getting thirsty just thinking about it
for my mind to go. It's not that type of day to drink. Uh, chocolate or cheese.
What's your favorite food?
Mexican's. Always the same ingredients. You're just mixing off.
It's like you got some guacamole, you got some sour cream.
You just mix 'em up and see they're in a shell. It's on a plate.
It's got some cheese. It's on nachos,
So. So who cooks at home?
Uh, I, I do, if it's, I do mostly,
or I don't know. I like, I like eating out a lot. Um, so does that count
If you cook at home, what's your signature dish?
TV or book?
Comma comma. Comic comic books are my go-to reading material.
I have the but not, but not, but not the latex superhero. We're gonna, um,
it's stuff like, um, like, um, there's the story. The,
there's graphic novels if you will. Um, they, but they can comic comic,
they comic comic reform. Um,
these are the ones that get made into fantastic TV shows later and people
realize, oh, it came from a comic book. Um, really compelling story saga.
If you're have to read one comic book in your life, read saga.
Right? I, I've not heard of it. So tell now I'm gonna have to look that up.
That up. So,
Star Trek or Star Wars.
Um, I, star Wars changed my life. I mean, I got that,
that's when I was that age where it's like, this is magic.
I'd seen Star Trek before on tv,
but then you see Star Wars on a big screen and it's like, oh my gosh. I,
I had given the choice to go read the book or see the movie and I thought, oh,
if I read the book, I can keep reading it again and again. 'cause I heard it.
It's like it was all in the news. Like, this is a great thing. Oh,
I'll just read the book so I can get that story story like a hundred times.
I can only go see the movie once.
We didn't have a lot of money when I was growing as a kid.
That was like the worst choice I ever had. But then the library club went,
so I got a chance to go with the library club and it's like, oh my God. What?
The book doesn't book doesn't always match.
Sometimes people say they know the book is always better. You know,
star Wars not so much.
It is, yeah. Proper theater at the cinema for that. And, and,
and do you know what?
I think you're the first person who's given a good reason to first Star Wars
over Star Trek. I'll give you that one. What,
what sort of music do you like to listen to?
Uh, nowadays a lot of, uh,
electronic dance music like, uh, elder Brook, Bob Moses,
stuff that you can dance to. Uh, it comes, it's just like a whole,
it's discontinuation from like the Cure and the Smiths electronic music back
then. Um, big alternative fan, um, alt rock. But,
um, now it's more, um, I have,
Yeah, I, I think we're gonna have to actually head out on a, at,
at cyto. So you said you
You said you listened to a DJ podcast. Did you know that John Ts is a dj?
I did not.
Uhhuh. So with any luck, we can get John to dj.
We should be, we should be made and Andrea should be at scientists.
We'll get Andrea and Sarah and everyone else up and we can have a,
a proper flow stars. Uh,
That would be awesome.
It is. That'd be really quite cool to have Jo. Oh,
we are nearly out of time and I've got loads of ques. Okay. When you retire,
if you could do any job for a day or a week short
burn, what job would you like to sample?
Sam. Oh, but that's hard when you come in cold, isn't it? Like I,
I'm gonna, I'm gonna, I'm gonna be a do a mix at a big rave.
Like with a 10,000 people I come in, it's like,
it's like when when it goes dead, it was like, oh, that would be,
that would be funny. So like two, two things.
Being on a stage in front of lots of people, huge rush music,
beef and everything, and then you drop the,
then you do the big drop and it was like,
So now we've gotta ask John to put a big drop in if we can get into DJ outside
to us, but, oh, come on. Lemme hit the drop.
Let's see if we get you up on there. DJing with him.
If you'd like show you some get you up there. Yeah.
Lemme, lemme do the drop.
Uh, do you know what I,
I think we have to keep these answers really short 'cause we are up to one hour
already. Tell me what, out of all your career,
when's been the most fun time
That was, that's so easy. Um, because it's,
everything's been building up to now. I am living my best life. Um,
thank like I could not be any happier doing cooler
stuff, having a better life than I am to today.
Um, and the impacts that I'm making today are gonna have,
make the people I work with are fantastic.
The organization I work with is fantastic. The ability,
the the changes that we're gonna enable across science are gonna be amazing. Um,
living my best life, doing the like, and the, my work life balance is now there.
Finally, um, when I started my company, it was like 24 hour day job. I loved it,
but it wasn't right. I, I loved being in that moment,
but it wasn't good for me and it wasn't good for my family and my kids.
Now I'm in balance and I, I have, it's
How, how do your kids reflect back on, how old are your kids now?
Um, my son is 21 and my daughter is 16. Uh, he's in university now, second year.
How did they
Reflect back on the That
Was a hard time. That was a hard time. Um, it, uh,
so something has to drop, right? I think this is,
this is a problem that we have a lot in our field. There's a lot of pressures.
Um, and when you're starting academia, I never wanted to be a professor. When I,
when I interviewed at BC Cancer, I forthright told them,
I do not wanna be a professor. Because I was fully aware, um,
through my relationship at the time with, with um, Fiona,
that she was just starting out as academic.
It is a 24 hour a day job if you allow it to be when you start out.
Because teach, you're just starting out teaching. You're writing grants.
It's just that you bring, you have kids in your lab that you're bringing up.
It will consume all your time if you allow it.
And then I started the company and I was, there's,
they were both going at the same time. Um, it's not good.
Something has to happen.
And then they come here and the place is like a bomb went off and then
the kitchen's a mess and it wasn't good for them.
And, but you must have justified that to yourself at the time.
This is a good thing that, right?
I I So you just, I just have to get through this.
I just have to get through this. I just have to get through this. The,
the light, the light's going, it's gonna get better.
It sometimes it takes a long and it takes a long, we,
I think when all the professors have been through that and it is tough and you
just have to get through that. But you have to,
there's only so many times in the day. And that,
and that work life balance is a real struggle enough field and a huge
shout out to all the moms who are professors who've done this.
They're the primary caregivers and that is a whole other
burden. And so, uh, just a huge shout out and respect to all the moms,
mother, professors, CEOs out there. Um, I cannot even imagine.
And on that, we are over the hour mark. I cannot believe that is,
that has gone so quick.
But I'll say everyone who's watched and listen today at Play Stars,
uh, just go and tune into Andrea and Sarah, for example, to start with. Uh,
and John t's a dj, uh, the one and only, uh, super cool.
Hopefully we'll see you at Isaac and Cyto. Uh,
I will drop another one through the other podcast Microscopies.
'cause Emma Lundberg with their Eve project and also, uh, Chris Lintott, uh,
who does the Zuni verse, another citizen science who actually,
if you want to make a difference and you just enter into citizen
science, you've got Eve, you've got Zuni verse. Uh, yeah.
Lucy Collins's podcast talks about drawing round electron micrographs,
electron microscope images. Imagine how boring it sounds, but super cool.
A difference that you can make, which is great. But Ryan, your energy,
I think I have energy. Oh my goodness.
I've been out energized on one of my own podcasts. That's amazing. Ryan,
you are an inspiration to us all. Thank you so much.
And I can't wait to buy you glass of red wine at ci and
The same in return. And that's how cyto parties start.
Thanks very much everyone.