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Deirdre Meldrum: Okay. How is that? Can everyone hear me all
right? Okay. Thank you.
So you'll find that our CEGS is very different from the one you just heard about with David.
And in our CEGS it's called the Microscale Life Sciences Center. We started at University
of Washington in 2001; you saw on his chart that we were one of the first CEGS in the
program. And we moved the headquarters of it to ASU, Arizona State, in 2007, when I
went there to be dean of engineering. And our goal here is to develop microscale modules
so that we can analyze live single cells and perform multiple parameter measurements on
them, and correlate those events with genomic information so we gain an understanding of
disease. Since we're analyzing live cells in chip formats, we call it "Life-on-a-chip."
And how is our Microscale Life Sciences Center a CEGS? First of all, and you'll hear some
of the same things you heard from David, we could not have done the things that we've
done in our center with RO1s. We've been pioneering new technologies for live single-cell analysis
to understand some life and death pathways and correlate those with disease -- whoops
-- and focused on coupling these metabolic measurements with genomic and transcriptome
analysis.
It's a highly multi-disciplinary, collaborative environment. In fact, we have very discipline,
you can imagine: on engineering, physics, chemistry, genomics, material science, computer
science, you name it. And a multi-investigator team, and also multi-institutional. That's
not a requirement, but that's how our center is organized.
We have what we think is a unique training platform for students and new investigators.
We are flooded with applications on a daily basis from people wanting to be students in
our lab. And people that work in the lab and in the center talk about how they love the
environment with all the different disciplines and the combination of the experimental work
and theoretical work. We have worked on and increased pool of genomic scientists and engineers,
and the numbers of unrepresented minority professionals in genomics and stem fields
through the Diversity Action Plan. And when I was at the University of Washington, Mary
Lindstrom and I created a new course on biology and genomics for engineers that's still there
as a regular part of the engineering curriculum.
This multi-investigator, multi-institutional; we have investigators at Fred Hutchinson Cancer
Research Center, Arizona State University, University of Washington, and the Fred Hutchinson
-- I mean, Brandeis University. And at these different institutions, in particular University
of Washington and Arizona State, at UDub, we have very tightly engaged with the engineering,
along with the medical school and genome sciences. And at Arizona State, we're housed in the
Biodesign Institute, and that's also a very multi-disciplinary environment with engineering,
life sciences, and so on.
We've been building this interface between the biomedical applications and the technology.
You can see here how the investigators kind of map out here in terms of the biology and
the medical, or the diseases that we're going after. And then a series of technology developments.
And we're constantly driving each other to advance in the technology and to be able to
answer new questions in terms of the biology. And also the biologists keep the engineers
on track so that we're developing something that's hopefully useful in answering biologically-relevant
questions. We could develop all kinds of cool technologies that would have no use [laughs]
for the biology.
Our goal is to understand, predict, and diagnose cell life and death pathways. Challenge here
is inherent heterogeneity in the cell populations, and this heterogeneity is believed to underlie
transitions to different disease states, in particular, in cancer and inflammatory-based
diseases. So the approach is to develop these dynamic, real-time, multi-parameter analysis
of single-cells, and apply this to fundamental problems in biology and health.
We have two model systems that we focused on. Pre-malignant progression of Barrett's
esophagus. That's with Brian Reid at the Fred Hutchinson Cancer Research Center; he's been
studying that for decades and has patients that he's also followed for decades. And then
pro-inflammatory cell death, or pyroptosis, with Brad Cookson at University of Washington.
And the model system there is salmonella infection of the mouse macrophage.
This is the vision that we laid out in about 2000, 2001, where we would start with a single-cell
and learn as much as we could about a single-cell; then go into cell-cell interaction studies,
tissue, and in vivo. And this is really kind of a 20-year vision, and we're really at the
cell-cell interaction stage, at the beginning of that, and some of our new things are getting
into tissue. But it's been very challenging in terms of the technology development at
the single-cell level.
So what we're trying to answer is why does a cell decide to live or die, and why does
it go down different pathways in cell life and cell death. There's apoptosis, which is
non-inflammatory cell death. And we focus on pyroptosis, pro-inflammatory cell death,
as well as neoplastic progression or cell life, cell proliferation.
This, in one slide, shows really the heart of everything in terms of what we can do with
the technology, and that's to perform stimulus response experiments on single-cells. The
stimuli could be anything like biochemical, infecting agent, or temporal cues to help
us understand metabolic networks, infection, cancer, and aging. Same kind of system could
be done for -- used for drug dosing experiments, you name it; it's very versatile technology.
And what we can do is start out down in the bottom left corner. Look at the state of a
cell pre-stimulus, and then track it over to a time with our sensors, and then do a
post-stimulus analyses.
So why do we care about individual cells? First of all, there's the intrinsic cell-to-cell
variability in tissues in vivo, and the cellular heterogeneity. And that's thought to be one
of the players in onset, progression, and also therapeutic response of complex diseases,
including cancer.
Most of the research has been done with bulk cell populations. And when you do that, you're
taking an average of millions of cells, and, actually, when we look at single-cells, there's
actually a lot of information that's being lost there, and you'll see in some of the
results that I show you why this is important. This is technologically challenging; there's
extremely low amount of analytes in the cell, and requires very challenging technical capability.
High detection sensitivity and specificity, high content, and high-throughput, due to
the variability. And then time-resolved non-invasive methods.
So when we were working on proposing our CEGS in around 1999, 2000, there was skepticism
about the biological relevance of the events at the single-cell level. The technologies
that were available were few. There's fluorescence-activated cell sorting, imaging cytometry, and so on.
And really not a lot of capable for analyzing life cells, live single-cells. Now if you
fast-forward to the last few years, there's -- the functional role of cellular heterogeneity
is recognized and widely accepted. And there are a lot more technologies. In fact, now
one of the Common Fund projects at NIH is on single-cell, and I participated in Francis'
Big Think in May of, what, 2010 or something like that, and that was one of the projects
that emerged from that. And if you look at the publications in 1999, those that had the
words single-cell in them, there are about 661, and there's triple that number now. And
Mary Lindstrom and I had one of the first publications in 2003 talking about cellular
heterogeneity and the need for single-cell analysis.
So in our technology, we can do live, dynamic measurements on a single-cell. One of the
important measurements is oxygen; that gives you the state of the cell and its health.
If you have a rapid up-shift or down-shift in the oxygen, you typically indicate some
kind of a stress. But if it's a slowly decreasing or some kind of degeneration, or slowly increasing,
some kind of proliferation. And then there are other things that we're interested in
measuring in terms of physiological parameters, and you can see those there, that membrane
potential, membrane integrity, various ion gradients, and so on. Then after we take dynamic
measurements of the cells over time and in a very controlled environment, we can fix
the cells and take snapshots of the DNA, RNA, and proteins.
So this is a diagram kind of showing the core technology. This is just one chamber that's
been in a microray, a microfabricated system. And this is to show you that we have put a
single-cell in a well that then has a lid. And in the lid, we can put in these different
sensors that are spaciously and spectrally separated. We also have ability to seal off
this chamber with a hermetic seal so that we can do reconsumption measurements, such
as oxygen consumption rate. And then those all get fabricated into a micro-ray and we
fabricate the sensors as well. Over the years, we've developed a suite of extracellular and
intracellular sensors; has been very challenging, you can't just go buy these off the shelf.
We have some really great sensor and development wizards that can make almost anything fluoresce,
and these are all optical-based sensors, but you can see the suite of them here. The ones
that we've chosen have been driven by the biological questions that we're working on.
And we have more recently had some dual and trial sensors so that we can more easily multiplex
those in the microfabrication process. And then these intracellular sensors here. And
we continue to build this suite sensor capability.
This shows the micropatterning of the sensors; we can place them really however we like down
to about three microns in size. Those are oxygen and pH sensors.
And this shows one of our devices in pH and oxygen sensors. And in this particular case,
we have clusters of cells. And here we show what it looks like when you have live single-cells
in each of the wells here.
Once we've done the dynamic measurements, then we can fix a cell, so these are the live
cells in the well. Then we can fix them and transfer them to some other module for some
kind of post-fixation measurements. For example, we can measure the mitochondrial copy number
or the trans-gene transcripts. And the process is shown here. It's pre-amplification free,
and we can use this for doing transcriptome analysis, as well as the mitochondrial DNA
copy number.
So I'm going to show you some of the results on each of the model systems, more extensively
on the Model System One: Barrett's Esophagus and Esophageal Cancer. Barrett's esophagus
is a precursor to esophageal adenocarcinoma. It's caused by the insult of acid reflux in
the esophagus, causes a chronic inflammation environment, causes damage to the cells in
the genes, and selects for genetic variants in the population. And then what it does is
it causes this change in the epithelium in the esophagus to have this columnar epithelium,
similar to what you have in the intestine, so there's these crypts. And the incidence
of this has been increasing dramatically; in fact, there's been in increase in incidence
of over 600 percent over the past 30 years. And you can see Barrett's and progress to
adenocarcinoma do not have a good survival rate.
This is a model of clonal evolution that's been around for decades showing the selection
of variants, variant populations. And the question is, why do these cells survive? So
Brian Reid and his team at the Fred Hutch have developed a set of cell lines; they have
the primary cells and have biopsies that they've taken on periodic basis of many different
patients. But also develop cell lines where hypoxia is one of the things that will help
give you a condition that's common in inflammation; it's a selective pressure inflammation. So
what we can do is create cell lines that then give the same conditions as you would see
in vivo and in various patients. So by doing selective rounds of hypoxia, we can create
the cell lines CPA, B, C, and D, that have metaplastic and various forms of dysplastic
conditions and these different mutations. So we use those in our studies.
And our goal here is to understand clonal evolution in Barrett's esophagus, looking
at the dynamics of clonal evolution by correlating the phenotypic and genotypic alterations.
And we have experiments in both cell populations, looking at the metabolic profiles, growth
rates, and so on. And then a series of single-cell experiments looking at the metabolic profiles,
mRNA expression levels, and so on.
So some of the data here, this is to show you what the raw data on the oxygen consumption
rate measurements look like. These are the CPA metaplastic cells, and the CPC are the
dysplastic cells. Each one of these curves is the oxygen consumption rate on single-cells.
So this is the rate here on the Y axis and then time along the X axis. So you can see
here, extensive heterogeneity, and also just in this data shown here, that we have a little
bit less variation in the oxygen consumption rates for the dysplastic cells.
If we look at more of these cells here, we see now this is just a different way of looking
at the data, the CPC cells are in the white bars, but then the CPA cells, we seem to see
a sub-population here. Statistically, it is like a double gassy inherit [spelled phonetically]
that's emerging. And if you were to look at the averages of the oxygen consumption rates
for these cells, you wouldn't see this difference.
In this case, we are taking cells that are metaplastic CPA cells, and then we have a
set of hypoxia-adapted cells; these are cells that were selected -- sent through six rounds
of hypoxia conditions, 20 hours each time. Then we grew them in remoxin [spelled phonetically]
conditions for a month. And then we looked at the oxygen consumption rates, and what
we find here are that the hypoxia-adapted cells don't vary quite as much and have a
lower consumption rate. And this is all consistent with the theory in the clonal evolution.
We also complement our studies with other kinds of themes. This is a FACS DNA aneuploidy
analysis, where we're, again, in this case, looking at metaplastic and dysplastic cells,
and subjecting them to different rounds of hypoxia. And what we see is that there's an
enrichment of a different population in these dysplastic cells, but not in the metaplastic
cells. So, again, that's something else that you would see because we're adapting to the
hypoxia condition.
And this is some SNP array data, again, showing the same kind of thing. This is alterations
in the copy number. Again, as we go through different selected rounds of hypoxia, we see
the enrichment of these different clonal populations.
And then we can do, as I mentioned, the multi-parameter measurements. This is some data on the C-D
cells where we combine oxygen, pH, and mitochondrial membrane potential into each well from multi-parameter
analysis on the same cells. And these cells -- this is a cell cluster, so about 45 cells
per well. And you can see here that even with the 45 cells in a well, we have a lot of heterogeneity
here in variation in these different parameters, and also kind of a delayed response in the
mitochondrial potential as the -- as you have the lack of oxygen, because we've sealed the
well, and then the hypoxic experiment.
This is looking at multi-parameter data on the live single-cells. These are CPA cells,
and, again, loss of heterogeneity and variability among the measurements. And again, the mitochondrial
membrane potential is collapsing here at different oxygen levels.
So what kind of -- you can how it's very difficult to start drawing conclusions on this; we have
to do hundreds of cells for each one, and we've done the statistical analysis to see
how many you have to do on depending on what kind of experiment you're doing. In this particular
case, those were all the -- those were dynamic cell measurements complemented with some of
those other measurements that I showed you. And then with the technology where we can
do the transcriptome analysis, we can also look at the mitochondrial DNA copy numbers,
same approach. And this is showing the measurement of the mitochondrial DNA copy numbers in the
CPA and CPC cells. And we see the -- first of all, there's a lot of variation, that they're
different. And there's a publication showing that if you looked at the bulk cell populations,
you've seen no difference in mitochondrial DNA copy number.
With that same technology we're looking at gene transcription changes in response to
hypoxia. These are mitochondrial-encoded genes; we selected some that are in the hypoxia response
pathway. And what we've done here is a short-term hypoxia experiment, so the controls of CPA
and CPC; CPA and CPC are the control. And then we subjected them to a hypoxic condition
for 30 minutes, and looked at the transcript before and after the hypoxic experiment, and
see that we get up-regulation of some of the genes here, including 16s, which is highly
conserved and is often used as a reference in bulk cell populations. But, in that case,
we see up-regulation of that one, as well as COX1. And these were in the CPA; we don't
see as much change in the CPC, so maybe there's some adaptation or something, some compensation
for the reduced oxygen in less ATP production.
We've also, more recently, been getting primary samples from the Mayo Clinic and do the experiments
right when we get the samples. These are just a couple of transcriptome factors, just to
give you an example what we're doing here. These are some of the bull cell results here,
looking at the CDX2 and the VEGF. But then when you look at the single-cell data, you
begin to see tremendous heterogeneity. And in particular, the VEGF, we see a couple rare
variants here; you would never see any of this when you look at the transcripts on the
bulk cell populations.
And now if we combine these -- what I'm showing here is data performed on the same cell. We
took a dynamic measurements, in this case oxygen and consumption rate, and then looked
at the transcript, the VGF -- VEGF transcript analysis here on this very same cell, and
then overlaid those there. So what we're seeing here is that in those cells that have much
lower oxygen consumption rate over here -- over -- wait. [laughs] Higher oxygen consumption
rate have a very low transcript level for the VEGF and vice versa. So there's very strong
negative correlation here. And so what we're seeing is, perhaps, in this condition with
the lower oxygen consumption rate and the high VEGF, that they may be more resistant
to hypoxia conditions, and they're -- just an idea of how would you use this data, of
course, there has to be a lot more done on this, but perhaps the fraction of those total
population -- the fraction in the total population of cells could perhaps be used as some kind
of signature for risk stratification of progression to esophageal adenocarcinoma. So these are
the kind of things that we're trying to get to in terms of what are these biosignatures
and what are these different mechanisms occurring in pre-malignant progression of Barrett's
esophagus.
So some of the conclusions on this short-term hypoxia experiments. That the dysplastic cells
exhibit features commonly found in transformed cancer cells, indicating early manifestations
of cancer-related phenotypes. The dysplastic cells are much less sensitive to the short-term
oxygen deprivation in terms of the transcriptional activity. And based on the differences in
the single-cell distribution parameters, the differing gene transcription regulation mechanisms
may be in place in dysplastic versus the metaplastic cells.
And then in the long-term hypoxia experiments, we've demonstrated selection acting at the
genome and, for the first time, in the metabolic level using single-cell analysis. And at this
hypoxia insult selects for sub-populations of cells with inherently slow respiration
rates, which then results in reduced heterogeneity and the metabolic rates. And also that the
hypoxia insult selects for clones with specific genotypes, again corroborating this notion
of clonal evolution in cancer.
We also have been doing some cell-cell interaction studies. This is just the example showing
what happens if we put one, two, or three cells in a well and measure the oxygen consumption
rate. And what you see here is a non-linear increase in the oxygen consumption rate when
you go from one to two to three.
And then another thing we're looking at is the interactions on the metabolism in the
gene expression levels when you have different kinds of cells or the same kinds of cells
together. So here we transused [spelled phonetically] the CPD cells, the dysplastic cells with green
fluorescent protein; did a similar thing with EPC-2, those are the normal cells with a red
fluorescent protein. Then we grew out mono-cultures, and then because they're fluorescently coded
here, we can very carefully design our experiment and grow up a co-culture. And then we took
those out and selected 30 cells of each, and then did next-gen sequencing on those. And
these are RNA-seq results showing the pathways most affected by these interactions of the
mono-culture versus the co-culture. And what we can see here are the pathways that were
most affected by these interactions are the ones that are key in self-survival and proliferation.
So these are the ones that came out on top. So, as you might expect, the interaction between
the different cell types significantly affects the function in life-death pathways.
I mentioned mitochondrial-encoded genes and our interest in mitochondria; it's a powerhouse
of the cell and very important. And with Larry Wangh at Brandeis, he's been developing a
variety of different technologies for -- that are all PCR-based, and we've been doing studies
on the mitochondria and in the Barrett's cells. And this is even more challenging than, I
guess you would say, than the single-cell studies because every cell contains thousands
of mitochondria ,and mitochondria contains up to 10 genomes, and each genome contains
16,000-plus nucleotides.
So Larry Wangh and his PCR-based technologies, LATE-PCR, linear-after-the-exponential Primer
Safe, to minimize mispriming and LightsOn/LightsOff methods. Don't have really time to go into
those. But this is an example showing the point mutations and three-gene targets looking
at bulk cells. And then when we look at the single-cells, these are CPB cells, so early
dysplastic cells in the Barrett's cell lines. And we see more variation here in the HV2
and the COX2 than we do in ND1. And this is one that's conserved in the mitochondrial
DNA.
So there are a variety of conclusions on this mitochondrial DNA heteroplasmy. But, in particular,
the technology has been developed to make it truly possible to study the mitochondrial
DNA heteroplasmy at the single-cell level. And we're working on experiments that looking
at this in single-cell but also in the single molecule, the mitochondrial DNA.
Now in model system two -- am I way over time?
Male Speaker: [inaudible]
Deirdre Meldrum: Oh, good. You don't know. Okay. [laughs] I
will try to hurry. This one will be very quick.
In Model System Two, this is Pyroptosis Pro-inflammatory Cell Death, this was -- is with Brad Cookson
with -- at University of Washington, he's an expert in this. And this is an important
process to understand because this inflammation is known to be involved in many diseases:
cancer, cardiovascular disease, stroke, you name it. And so trying to understand the mechanism
of and how this how inflammatory process works.
So our hypothesis is the heterogeneity in the cellular physiological states influence
the life and death decisions and phenotypically manifest as resistance, susceptibility, and
intermediate states in response to environmental stressors. What happens in pyroptosis is some
activation of caspase-1 DNA damage, pore formation, secretion of inflammatory cytokines, and then
swelling and ultimately release of the inflammatory intracellular contents. And what we're interested
in is trying to understand the temporal events in the pyroptosis pathway, in particular,
define the roles of the cytosolic and mitochondrial potassium during pyroptosis and the signature
of ATP concentration changes of the cells undergoing pyroptosis, which result in different
physiologies. And then that combined with whatever kind of stimulus, what is this cell
death response in the biological output, in particular, inflammation, and how does this
occur in different diseases.
So with Brad, there have been a variety of conclusions. There have been a lot of things
that have been worked to get everything ready for the single-cell analysis and taking advantage
of a variety of different genomic technologies. And some of the work there has resulted in
heterogeneity at the level of stimulus can generate distinct physiological outcomes.
And that's done with a FACS analysis. A set of genes involved in the distribution of active
caspase-1 has been identified; how the active caspase-1 is -- diffuses, and has -- goes
through the cytoplasm has been figured out. There's a bunch of other conclusions here:
temporally map the key biochemical events of pyroptosis in single-cells. And ongoing,
utilizing the ATP sensors, the potassium sensors, and also the ability to do the mitochondrial
membrane potential measurements. We're continuing to do a variety of experiments here to gain
a better understanding of the pyroptosis pathway. And you can imagine there, I've talked about
inflammation in Barrett's esophagus, and there's connections there and many more experiments
that could be run there.
So that's kind of a quick run-through of the different technology development and biological
problems that we've been addressing with the technology. And then the Diversity Action
Plan has been an important part of this. The director of this since 2002, when we first
started it, was -- is Lisa Peterson, and she's been marvelous. And Betty and others can attest
to that, but she's really done a super job with this. This is really data from University
of Washington and that part of all of this. But we've had 28 graduate students all in
STEM. We currently have 116 undergraduates in the program. And then when we look at where
our undergraduate alumni, what they've gone on to do, large percentage of them working
in STEM and science. And some of them gone on for Ph.D.s, enrolled in MD/Ph.D. programs,
completed MDs, and so on. So really, I would say, very successful program. And the environment
of the CEGS and also the other programs at the universities have made all of this possible.
As some of the results, we have incoming freshmen in a program called ALVA, and they've had
a very high success rate of, first of all, coming into college, and also going into stem-related
fields. And we've had five Native Americans and Hawaiians earn Ph.D.s, and four former
graduate students now in faculty. And all the different students that have participated
in the Diversity Action Plan combined have given -- have 810 publications and presentations,
which is really quite astounding.
And you heard Jeff and David mention about dissemination and how do we gauge success,
or utility, or use of what's developed in a CEGS. And ours is very technology-heavy
or centric. So we have been, over the years, working on our patent portfolio. This is our
sensor or intellectual property or tree or invention tree where we continue to work on
our disclosures and build that patent portfolio. We have 93 peer review journal publications
in press -- or published and done, and participate in a variety of conferences and workshops.
So now what about beyond our CEGS? You don't want to just end. There's so much more to
do. You saw we have, like, a 20-year vision. Well, some of the things that we've done is
we have now been funded by LINCS, and I'll talk a little bit more about that as a Common
Fund program. We more recently received a grant from the Common Fund single-cell program,
where we're doing laser lysis of single cells. There's a two-photon laser lysis system to
go directly from tissue, keeping the spacial information, and going to a QPCR system, is
our race-track thermal cycler that we've developed. And this is a really exciting project because
our end-goal there is going take one of those crypts from Barrett's and do a full analysis
of QPCR on the cells from the Barrett's crypt.
And then another thing is combining the technologies that we've developed in the CEGS with the
other technologies. So, for example, we have one of the only -- we have the only research
instrument from the company called VisionGate called Cell-CT, it's cell-computed tomography,
does -- creates 3-D images of cells, these are fixed cells, for looking at cell morphometry;
it can quantitate over 800 different parameters. And then with funding from the CEG [spelled
phonetically] foundation, we're developing a next generation instrument that'll be doing
3-D image and computed tomography on live single-cells. So my dream is that we'll be
able to take those cells and do the physiological measurements from our CEGS, the morphometry
measurements, genomic, transcrimic [spelled phonetically], all the different omics, and
really interrogate a cell in all the ways possible.
So just quickly on the LINCS, what we're doing here is this is directly building on our Microscale
Life Sciences Center, the CEGS, to develop the next generation system that's much higher
throughout. The goal is to have -- to be able to process 10,000 cells in what we could now
call a cellarium. And that this becomes part of a system, and in LINCS, this Common Fund
project, this is to build a database with the cell data. And what we're adding to LINCS
is the dimensions of single-cell and time.
And in the future, this is my grand vision for all the things I work on, which is biosignatures.
All these things I've been talking about play into this, but the goal is to really transform
healthcare, the concept of biosignatures for presymptomatic diagnosis and actually prevention
of diseases through biosignature discovery, figuring out what of those omic, and imaging,
and behavior elements that are important and predictive of someone's future health status,
and then standardization, qualification, clinical validation of these for precise health and
improved quality of life.
And this is the team that's worked on our CEGS over the years. I'm extremely grateful
to NHGRI and the CEGS program for this opportunity to provide a really unique environment and
opportunity to develop technologies and work on science that we really couldn't do otherwise.
Thank you.
[applause]
Male Speaker: Want to answer my question?
Deirdre Meldrum: Yeah.
Male Speaker: [unintelligible] to hear how these things
evolve from initial conception.
Deirdre Meldrum: They do, they absolutely do evolve. So when
we first started in, say, around 2000, and in our first proposal, we had so many applications
that we were going to apply our single-cell technology to, but we hadn't even developed
the single-cell technology yet. And we were working -- we had an investigator on ***/AIDS,
yeast to study aging, you name it. We had all these different applications. And with
our -- we had a scientific advisory board that was constantly advising us, and we'd
seek their advice. And they kept saying, "Focus, focus, focus." And you saw on Jeff's slide
about focus. Well, that really helped us a lot because we really focused on getting technology
that would be -- that would work. And then, as you saw, we have those two model systems
and we've stayed focused on those. I have people ask me all the time, "Well, can you
apply it to this? Can you apply it to that? Can you apply it to this?" Which is great
and we want to do that, but we've really remained focused so that we could get the technology
development to this stage where you saw now with where we are and with the LINCS program,
that we can produce data that's useful, and we really are at a stage where you can take
on more collaborative projects. And in the LINCS program, we're on just the beginning
of a collaborative project with Columbia on looking at oxidative phosphorylation, for
example.
So that's -- they evolve. And thankfully, the program allows this kind of flexibility,
but you also get a three-year review and then a competitive renewal. And in our competitive
renewal, by that time, we had certainly gone down to the two systems and had them much
more focused CEGS.
Jeffery Schloss: So I just want to add to that. So this was
-- I mean, this was really hard, right? These are your collaborators and they're part of
your grant. But then you have to make decisions. And so in addition to the biological applications
that they had to decide which ones they were going to take forward, they had some fantastic
technology development investigators, right?
Deirdre Meldrum: Oh, yeah, and we had a --
Jeffery Schloss: And --
Deirdre Meldrum: -- million other technologies that we have
been developing that we had to make decisions --
Jeffery Schloss: Had to make decisions.
Deirdre Meldrum: -- more than once that would, yeah, that's
true.
Jeffery Schloss: Okay, so we're going to come back to an issue
about that when I come back up.
Deirdre Meldrum: [laughs] I know what that one is, I think.
Female Speaker: I wanted to ask. So you made a comment at
one point, I think, earlier on that you thought of this investigation as a 20-year process
and you were about halfway through.
Deirdre Meldrum: Yeah.
Female Speaker: So just speaking to -- if you could speak
to the way the program is structured, that it's five years and five years, and potentially
10 years. And, you know, granting that all of us would rather have more funding than
less, can you talk about whether that five year/five year structure was a good structure,
or some other kind of arrangement might be better? And how did you maintain -- how are
you going to maintain funding after this now?
Deirdre Meldrum: [laughs] Okay, so I think five years/five
years I good. I think it's healthy to have a competitive review at that kind of a time
period because the fields are moving so rapidly, and it's important to, I think, revisit what
it is you're doing; it makes you think carefully about what you're going to do and you have
to write it down in your proposal and say, "I'm planning to do this." So if you had like
a 10-year, I think that could be dangerous because you could just go all over the map
and maybe, in the end, not produce something so useful. I think the amount is a good amount
because, well, maybe a million more, two to three million -- two -- I'm talking about
direct costs. But I think it depends on what kinds of problems you're tackling and what
the scope is. There -- I see times where you're trying to do something that carves off some
neat piece of something, that maybe it would require a certain amount and you could scale
it that way. So maybe some flexibility in what people could come in with.
As you see all along I've been getting other funding, and I think that's also -- this provides
like a core, and it allows us to have a team that's, you know, very multi-disciplinary
and focused on certain things. But we compliment this with so many things, like that Cell-CT
technology, for example, we use for studies in the Physical Sciences and Oncology Center
at the National Cancer Institute. And there's different ways to leverage our drive, new
capability, like now this single-cell laser lysis thing is going to take advantage of
a lot of things that we developed here but take it to the next stage along this continuum
here. So [laughs].
Robert Nussbaum: You showed this patent pathway. I'm wondering
if you could talk a little bit about what, if anything, you're doing about commercialization,
and also about the issue of exclusive versus non-exclusive licensing by your institution.
Deirdre Meldrum: So we're -- we have a whole bunch of disclosures
and things we're putting in right now to complement what you saw there. And we work closely with
our -- it's called Arizona Technology Enterprises. They're pretty flexible in terms of how to
deploy things and what -- and do it on a case-by-case basis, whether it's exclusive or not. For
example, we've had commercial entities, of course, interested in our sensors, and they
want to just take those. And there's other avenues in other parts of our technology that
could easily be commercialized for that one thing.
But the other thing that I've been doing with the ACTE is to keep them informed about the
bigger picture of our systems so we don't -- so as decisions are being made by them,
that we're not losing something in the grand scheme of things. So they're -- been pretty
flexible about that. But we -- they have a whole process on advertising what the technology
is, trying to gain commercial interest and that way for commercialization, and so we're
in the process of that with some of our things right now. That answer your question?
Male Speaker: I was just wondering if you had plans or if
you have looked at the single-cell evolution of resistance to chemotherapeutic agents by
looking -- by exposing.
Deirdre Meldrum: We have not yet but we have a collaborator
at Mount Sinai and we actually had that as a project in a spore that we tried but we
didn't get. [laughs] But I guess those are hard to get. We tried just a one time; that
was in a lung cancer spore. But it is something that we're interested in, yeah.
Eric Green: Jeff? You're going [spelled phonetically].