Articles, Blog

An Epigenomic and Transcriptional Basis for Insulin Resistance – Evan Rosen

October 14, 2019

Evan Rosen:
All right, well thank you very much to the organizers for the opportunity to come and
talk to you today. What I’m going to tell you about is some work that we’ve done looking
at trying to understand the epigenomic and transcriptional basis because I think those
two things go really hand in hand of a very important medical condition that we’ve been
— in my group have been studying for quite some time which is to say insulin resistance
specifically complication of obesity and associated with type 2 diabetes. I don’t think I need
to belabor this point but I’m sure that most of you are really quite aware of the burden
— the medical, psychological, economic burden of type 2 diabetes and obesity in our country.
These are just CDC heat maps showing the increase in the prevalence of these conditions in the
last several years and you can see my maps only go up to 2009. They’ve been — I didn’t
put in the more recent ones; they’ve had to invoke all new color schemes because they’re
running out of red. There’s only so many deep shades of red you can get. This is not just
a major medical and physical burden on people but it’s actually been predicted — or been
calculated I should say to be one of the top three problems economically that the world
faces following smoking and armed violence and terrorism is obesity and metabolic diseases. So what my lab studies actually are fat cells.
There are many different cells that one could study in obesity: the brain, the liver, the
pancreas, et cetera. But we’re interested in adipocytes. And my lab has really been
asking, what are the critical transcriptional pathways that underline these key transitions
or differentiations or distinctions in adipose biology? And we’ve focused on for example,
what are the pathways that allow a non-fat cell to become a fat cell during a process
called adipogenesis? We’re also very interested in, how does a mature fat cell which is an
insulin sensitive cell stop listening to insulin? What happens to make it stop listening to
insulin and a corollary of that is what can we do to make it listen better? And also,
how do we turn white fat which is the energy storing cell type that we’re all familiar
with to maybe a brown fat cell type which maybe you may have heard of is a more energy
burning and can promote a cell type in which can promote health. So the way that we study
these critical questions actually, the way that we approach these things traditionally
has been to sort of, you’ll come up with a guess as to what a candidate transcription
factor that might be interesting for you to study would be; then you identify its target
genes and work on its function in that way. So you might do this because there’s a microarray
that you did or an RNAC experiment or perhaps someone made a knockout mouse for another
reason and the animal is fat or it’s skinny and they give us a call or a study that’s
been done in lower organisms and worms or flies that affects the larval fat body let’s
say. And then we go and look at the orthologous proteins in mammals. But there’s another way
to approach this problem, which is in a more systematic and comprehensive way, which is
if you can identify the cis motifs that are important — which identify regulatory elements
that are contributing to the regulation of the process that you’re studying whether it’s
adipogenesis or insulin sensitivity. And if you can then work backward to computationally
predict which transcription factors might be involved based on what the regulatory elements
that you see changing in the process that you’re studying then you can go and work on
the function in that way. And the way that we do that is essentially by Hanser [spelled
phonetically] mapping using clues from epigenomics. So in this case you could use a DNA methylation
status as your predictor. You can use non-coding RNA status in many cases but what we use in
my laboratory primarily are covalent histone modifications through chip seek and then identify
active enhancers. Or I should say enhancers that change their activity during a process
that we’re interested in studying and then we work backward as I said or as I just showed
to show that — to identify and predict factors that might be important in that. And I’ll
show you some examples of that. We published a study a few years ago in which we looked
at adipogenesis, we looked at both mouse cells and human cells that were undergoing differentiation
in a dish toward the fat cell pathway. We accumulated various histone marked chip seek
profiles at different time points of differentiation and those are shown for example here. We have
the mouse on the top and the human down below. Is there a pointer or how do I — do I just
use the mouse? Oh here, right here, I’m sorry. Right in front of me. Yup, so the mouse up
here. The human down below and you can see here that there’s promoter marks and various
enhancer marks for example that light up in this region and we can see that as we move
through time this is in a very early undifferentiated state and as we move through differentiation
we can see the appearance of new alternate promoters. We can see enhancers that disappear and enhancers
that appear and we can make various predictions and we can identify literally tens of thousands
of regulatory elements that appear to be a dynamic during this cell differentiation process.
And then what we do is we take these enhancer regions that are interesting to us, that are
changing and we compare the DNA that’s under those to databases of transcription factor
motifs and we can then predict transcription factors that might be important in this process.
And when we did this for adipogenesis we came up with two lists. One of them was based on
motifs — excuse me — one of them was based on enhancer peaks that disappeared during
differentiation so they were high enhancer activity during the pre-adipocyte stage and
then they disappeared during differentiation. And then the opposite pattern those that were
not present in pre-adipocytes but appear during differentiation. And everything — and those
all predicted different transcription factors that might be involved and everything I’ve
put a check mark next to here is something that was known from the literature at that
time to be involved in differentiation so you can see this provides an important sanity
check then that you should with this method be finding factors that you know are important
but we of course wanted to identify new biology. And in this particular study we focused on
these two factors here, serum response factor and PLZF — promyelocytic leukaemia zinc finger
protein — two very well studied transcription factors with a huge amount of literature but
not data whatsoever suggesting that they might be important in adipose tissue biology or
adipogenesis specifically. And so we were able to show that when we over-expressed
them we enhanced adipogenesis and what I’m showing here is that when we knocked down
these factors we could enhance — sorry — when we over-expressed them, we repressed adipogenesis
and when we knocked them down, we enhance adipogenesis. So you can see here the more
red the dish is the more fat has been accumulated and various marker genes of adipogenesis are
increased in these knock down cells. And so in this way we have now identified six novel
transcription factor pathways all of which are published that we have identified through
this approach, that we can use enhancer mapping to sort of tell us what might be important
for adipogenesis. I want to shift tension now to insulin resistance, and really our
study on insulin resistance was really focused on trying to answer two different questions
that have been bedeviling us for some time clinically. I am a clinical diabetologist
and I do take care of these patients and something that always really struck me was the fact
that you can see insulin resistance in a lot of different kinds of patients. So we know that it accompanies obesity for
example, but also one sees insulin resistance in patients that don’t have enough fat. This
is a gentleman with a form of congenital lipodystrophy and you can see his characteristic wasted
face. And people like this gentleman are very, very insulin resistant. People with various
hormonal conditions like the little girl up there on the top who has Cushing’s syndrome
and she has an over — she has a tumor that causes the over-expression of glucocorticoid
in her body. And the woman down below has acromegaly, a pituitary tumor that causes
the over-expression of growth hormone. And both those conditions are associated with
quite severe and significant insulin resistance. Also, neurodegeneration often associated with
insulin resistance as well as even some physiological conditions like pregnancy also highly associated
with insulin resistance and many other different disease states have insulin resistance as
sort of a common phenotype as well. You can model this in a dish by the way. You can put
fat cells, grow them up in a dish as I’ve shown, and you can pretty much point to anything
in the Sigma catalogue and dump it on top of it and it will cause insulin resistance. And that raised the question, to what extent
are the molecular pathways shared in these different conditions? And so you can imagine
then two different scenarios. One scenario is when you have sort of all these different
agents are causing insulin resistance in their own unique way, and then another possibility
would be that in fact they’re all working through some common mediator, some nodal pathway
that could be integrating the response to all these different agents. So the approach
that we’re using then is we take cultured adipocytes. These are mirroring adipocytes.
It’s a well-established cell culture model. They have very high fidelity and they do almost
everything that a real adipocyte does. And we treat them either with TNF alpha or dexamethasone,
and I’ll explain why in just a moment. But those are well known to cause insulin resistance
and so you get this rightward and downward shift then in the insulin response curve with
respect to glucose uptake. And what we’re looking at then are changes.
They could be transcriptomic changes or epigenomic changes or anything you’re interested in that
are associated with TNF treatment or dexamethasone treatment, and we’re going to focus on what’s
in this middle set then, what’s in this center piece there and look then to see if that can
help direct us toward common pathways. Okay, so why dex and TNF? Really the bottom line
is that these are well-established pathways. We know that if you give people or animals
or cells dexamethasone or TNF they cause insulin resistance. If you knock those things out
of animals, the enzymes, the receptors for glucocorticoids. If you knock out TNF or its
receptors you improve insulin sensitivity, et cetera. But really, the reason that we
did this is because they’re so very different. Remember, we’re looking for something in the
center of that Venn diagram and so you really want those two circles to be as pulled apart
as far as possible so that you have a very small set in the middle. That makes it a much
more tractable experiment. So, the real reason is that dex is the prototypical anti-inflammatory
agent. TNF is the prototypical pro-inflammatory agent and so we would expect on first principles
that they would have a very small number of biological responses that would be overlapping. So that’s the first question we wanted to
get at. How do so many different things cause insulin resistance? The second question was,
are there nuclear mechanisms of insulin resistance? There are literally tens of thousands of papers
on the mechanisms of insulin resistance, and my lab and many, many other labs over decades
have contributed to those, and they all focus on cytoplasmic pathways, pathways dealing
with signal transduction proteins, mitochondrial pathways, oxidative stress, ER stress, et
cetera, et cetera. And yet there is this sort of evidence that in fact we should be considering
other nuclear aspects to insulin sensitivity. For example, we treat insulin sensitivity
in the clinic by giving drugs that activate transcription factors for example. We also
know that these cellular models of insulin resistance such as I’ve just described don’t
work in hours. It takes days to weeks for them to really fully develop and that suggests
a transcriptional or epigenetic component. And we also know that there is this wealth
of data that links chromatin state and epigenetics to obesity and its complications and I’m referring
there to things like fetal programming and the developmental origin of adult disease
hypothesis where we know that depending upon conditions that you might experience in utero
or nutritional conditions that your grandparents or great grandparents might have experienced
that that has a profound influence on your risk of developing obesity and diabetes for
example. So that suggested again to us that there really must be some sort of nuclear
component here. So the model’s very simple. We take cells — these fat cells in a dish,
and we treat them either with dexamethasone or with TNF. We’re never treating them together.
These are just two separate treatments and we see that we’re causing resistance to insulin,
so this is just the measure of glucose uptake in response to added insulin, and these cut
that down quite significantly. On the right is just a time course of the
effect, and that just illustrates what I showed you, that you don’t get full insulin resistance
for about a week or so. And then at the bottom here what I’m showing now is that we are not,
with our regimen, effecting traditional signaling pathways. We’re also not effecting differentiation
of the cells. We’re not de-differentiating the cells because this is a very important
control in our field because obviously if you cause the cells to revert back to a fiber
elastic state, of course they’re going to be insulin resistant, and that’s a trivial
response and we don’t really want to study that. So we are keeping ourselves healthy
fat cells with our treatment regime. We looked at the transcriptomics of these — under these
two different conditions, and you can see that there are about 1,000 genes that are
up regulated when you add dex or when you add TNF but there’s only about — a little
less than 300 or so that are coordinately regulated by these two agents. And that again
illustrates the point that I made earlier that you really want to have a relatively
small and intersecting set to make that much more tractable to study. If there — if we
did TNF alpha and IL1, both of those will cause insulin resistance, but we imagine they’ll
have almost a completely overlapping set and that would be very difficult to parse apart. And his just shows that if we compare our
genes that are changing here — upregulation genes here — what we’re looking — and we
compare them to genes that are changing in garden variety obesity in an animal that you
feed a high fat diet, in fact, what we see is actually relatively little concordance
with the dexamethasone treated genes. A little bit better with the TNH treated genes, but
that intersecting set, those 271 genes actually are quite similar to the genes that we see
when we just look at garden-variety obesity. So then what we’re focusing on next is epigenomic
marks and we did six different marks I believe and maybe even a little bit more, but the
one I’m going to focus on here is histone 3, lysine 27 acetylation. H3K27ac which has
shown to be a mark of enhancer activity and that’s really what we’re deriving most of
our data from here, so we’ll focus on that. And specifically I’m going to focus on the
H3K27ac marks that change, that are upregulated, that are within 400 kilobases or so of genes
that are also changing in the same direction. So we’re looking at upregulated genes and
now we’re looking in the space around that and looking for upregulated K27ac peaks. And
we can imagine that there are several different patterns here. You can imagine that there
are sometimes you’ll find a K27ap that only changes with dexamethasone or it might only
change with TNF or it might change coordinately with both. And of course those are the ones
that we’re most interested in. If we look then at those peaks that are changing with
dexamethasone only, what we see is the top motifs that we cover under those peaks suggest
actions of the glucocorticoid receptor. Perhaps a very unsurprising result. And in fact, the
AR, the [unintelligible], the PR receptor that you see also in that list. They actually
use almost identical motifs as the androgen progesterone receptor and they’re very similar
steroid receptor motifs. And so what we can see here is that if we see places where dexamethasone
is changing the epigenome, it seems to be doing that perhaps through the glucocorticoid
receptor. Similarly, if we look at places where TNF by itself is affecting the epigenome,
the top hit there is NF kappa B and I think that will surprise absolutely nobody. But what’s interesting is if we look at this
intersecting set the ones that were the dex and the TNF were causing similar changes,
what we see again is that the top motif was the glucocorticoid receptor, and we know by
permuting the labels that this is not just because the dex set is part of this. We know
that this is truly, statistically significant for this intersecting set. We also see some
other interesting motifs there, and I’m going to focus on two, the GR and the VDR, which
is the vitamin D receptor for the rest of this talk. So what we first of all were focused
here on the glucocorticoid receptor motif, and we know then that dex works through the
glucocorticoid receptor. It is a synthetic glucocorticoid, and that causes insulin resistance.
But our work here suggests that TNF also activates enhancer marks that seem to have a GR motif,
and it raises the possibility then that TNF could be working through the GR. This is a
fairly heretical notion by the way. Certainly was very heretical to the inflammatory reviewers.
I meant that they study inflammation not that they were inflammatory, although some of them
were. [laughter] But what we could see is that his pro-inflammatory
cytosine could be activating at an anti-inflammatory nuclear receptor. So this is just an example
of what those tracks might look like here. Just to clue you in, dex treatment is always
going to be in blue. The untreated in black and TNF always in read just to make things
very simple here and what we’re seeing here is, also these tracks show the time course,
two hours, 24 hours, and six day treatment and you can see here that there is this cluster
of genes, Tmem176a and b. If anybody knows what these do please let me know. There’s
really almost no literature on these two genes. But what I can tell you is that they are induced,
or I should say they’re enhancer induced by dexamethasone up stream and that same enhancer
seems to be activated by TNF and there is a GR motif there. We can show by chip PCR
that in these cells when we treat with dex and TNF that we can chip the GR preferentially
off that spot. We actually went to an entirely different model — cellular model — this
is primary fat cells from animals that we differentiate in a dish, not a cell line,
and we treat them with dex and TNF. We also cause insulin resistance, and we also see
those same ability to chip GR off that exact spot. And then I think most importantly, if we just
go right into an obese animal, take the mouse, isolate its adipocytes, and check out where
the GR is binding, in fact, the GR is binding right at that spot as well. Not to belabor
the cell biology, but I think this is very interesting. It points to the sorts of biological
insights one can get from these epigenomic data. We know that the GR normally resides
in the cytoplasm. What happens after dex binds to is it moves to the nucleus and so it translocates
in response to ligand and you can see that here that in response to dex you see an increase
in the nuclear amount of GR and a depletion in the cytosol of the GR. This has been shown
by many others as well and what we see here with TNF is we also can cause nuclear translocation
of the GR. This is without adding and exogenous glucocorticoid. And we see in fact though
that we’re not depleting it in the cytosol. We seem to be increasing in the cytosol as
well, suggesting that one of the effects of TNF is to increase GR levels in the cell,
and we’re looking at this much more closely now in a sort of more rigorous cell biological
way. We did a chip seek of GR in response to dex
and in response to TNF. We can see that TNF causes about ten-fold less translocation or
ten-fold less, I should say, binding of ten-fold less bots in the genome than does dex activation
of the GR. And so what we can see though is that those sites largely area subset of the
GR sites, and we’ve been looking now trying to identify, what makes those sites special?
We think we’ve identified a co-occurring motif that directs the GR once it’s been activated
by TNF to those spots. And then this for us is really where the rubber meets the road;
Nr3c1 is the GR gene. We’ve knocked it down. I’m here with four different hairpins and
what one show is that we can rescue completely dex included insulin resistance. So if you
get rid of the GR, dex can’t work anymore. That’s not surprising, but what is surprising
here or what is very hopeful for us is that when we knock down the GNR, we get the full
ability of TNF to induce insulin resistance. So TNF can still do about half of its thing,
but it loses about half its potency. So that does suggest to us then that TNF is working
in fact directly through the GR, and we have a variety of cell biological and biochemical
studies going on. Phosphoproteomic analysis of the GR in response to TNF. We’ve identified novel phosphorylations of
sites and I won’t go into all that but just to say that all of that really came from this
epigenomic analysis that we did at the beginning. To suggest what has been a really heretical
hypothesis about activation of an anti-inflammatory factor by a pro-inflammatory cytokine. Another
factor that popped up on our list was the vitamin D receptor. This was very interesting
to us. There are connotations in the literature that vitamin D might be involved in literature
sensitivity. But in the opposite way. It’s something we can discuss later if you want,
but the bottom line is that we were wondering is could the VDR be involved here? And in
fact, what we can show is that here is another gene of interest and here’s a — you can see
a peak here. K27ac peak that’s induced by both dex and by TNF and it has a VDR motif
and we can chip VDR off that location. I’m not showing it here, but in this other cell
model also we see VDR in the same location, and in obese animals VDR is also chippable
from that location as well. And again, if we over-express the vitamin D receptor, we
cause insulin resistance, and if we knock down the vitamin D receptor, we can prevent
the full on set of insulin resistance. We restore a significant portion of insulin sensitivity
in response to dex and TNF. Interestingly vitamin D receptor is elevated
in the adipose tissue of obese animals, different genetically obese models, high fat fed animals.
It’s also elevated in the cells when we add dex and TNF. VDR is itself one of those 271
genes and we can also show that it’s regulated by the GR. So the TNF and dexamethasone both
turn on enhancers that regulate the vitamin D receptor. You can see they’re different
enhancers. The dex is activating through here, TNF through here, but they both have GR motifs.
We can chip GR off both those sites in response to dex and TNF, and we can see binding at
this site in the animals that are obese. And so what we see then are a network we’re beginning
to develop about how the epigenomics really can lead us to novel hypothesis about transcription
factors and how they may be interrelated. In the last few minutes, what I want to talk
about is new data that we’ve been — everything I’ve just shown you has just recently been
published in [unintelligible] cell biology but what I want to talk about in the final
moments are new data about humans and what we’re doing here. So we’ve tried to take the
same paradigm and apply it to humans if you will. This involves collecting adipose tissue.
We are fortunate in that adipose tissue is the one tissue that people are willing to
give you an awful lot of. So, if we were doing neurobiology, it might be a little more difficult
to convince out patients, but we do get between one and ten kilos of fat at a time from patients.
That involves a fair amount of processing. We’ve had to invest in industrial sausage
grinders — I’m not kidding — to get through this stuff very quickly. And we can isolate
adipocytes as you know any tissue — an adipose is no exception — is complex, it’s made of
different cell types. so another advantage that we have built into the adipose tissue
is that adipose and mature adipocytes float when disassociated from the rest of their
cellular matrix and so we can use sort of this low-speed centrifugation method after
collagenase treatment to isolate the mature adipocytes away from the other cells in this
heterogeneous tissue, and then we can use the pure adipocyte. Then we isolate the nuclei,
crosslink that, and we can then use that for chip seek. And what we get are these very beautiful profiles.
I’m showing some — one of our profiles up here on the top, and you can see ENCODE data
for adipose tissue in the purple line right below it and one thing you can see is you
can see these sort of macrophage markers in the ENCODE data because of course the ENCODE
data is a whole tissue and it includes a huge amount. Fifty percent of the cells in a fat
pad are not adipocytes and they’re made up of immune cells and epithelial cells and other
things, and you can see here that we see the ubiquitous markers. We get a much more robust
representation of the adipose tissue marker, the ones that truly belong in the adipocyte,
and we basically lose them macrophage markers as well. So we’re very pleased that we also
lose the pre-adipocyte markers. So we’re very pleased with sort of the quality of our data.
What we can see is that we can begin to — and for a large number of novel biology here,
we’re seeing novel transcripts, alternative start sites, things that are popping up in
gene deserts that seem to be important in adipocytes. So what we’re going to have very
shortly now from — literally we’ve now done dozens and dozens of patients — is a much
more detailed epigenomic map of what those adipocytes look like. But specifically what we have is now we’ve
collected patients across a spectrum of insulin sensitivity, so we know the patients and the
top and the bottom quartiles of insulin action based on a technique that we use called HOMA-IR.
And what you can see then is that we can already, in this sort of small sample that was done
a little while ago, you can see that we’re already identifying enhancers, for example
right here, that are relative — seem to be highly active in insulin sensitive patients
but much less active in insulin resistant patients. There are some opposite ones as
well where there seems to be more activity in insulin resistant versus insulin sensitive
patients. And we’re seeing literally hundreds of such differentially regulated peaks now
in many more patients than we have here. The ones I’ve shown here on the left, these represent
biology that’s pretty well known: adiponectin, GLUT4, other adipocyte genes that we know
are involved in insulin action. What we also find are differentially regulated peaks in
genes that — some of which have absolutely no known biology in any system. Some of them have no known biology in adipocytes,
and so we’re really trying to put this together now. And we have, I should say, in the last
week hot off the presses, we think have identified novel factors that might have been involved
in insulin resistance in human disease. So, to summarize what I’ve shown you then, dex
and TNF can cause discreet changes in the epigenome of 3T3L1 cells. These are cultured
adipocytes that associate with insulin resistance. We can use motif finding in these differentially
regulated regions to identify novel pathways, allows us to do a lot of cell biology. Some
of what we found is that TNF causes insulin resistance in part through an independent
ligand-independent activation of the GR. Also the vitamin D receptor seems to be important.
In data that I didn’t show you, we’ve actually identified some of the downstream target genes
of these factors, so we’ve identified now at least four genes that when over-expressed
in fat seem to cause insulin resistance. They are GR and VDR targets in response to dex
and TNF, and we have our human studies undergoing — ongoing, which we are actively recruiting
for and working on and which appear — fingers crossed — to be yielding some very interesting
data. So with that, I want to thank the people who
did the work. Most of the work I showed was done by really two talented post-docs, Sona
Kang and Linus Tsai and Yiming Zhou, who was our computational biologist. Other people
in the lab contributed. We had help from our collaborators and our friends at other institutions
including the Broad Institute where I’m also a faculty member. And of course, funding from
the roadmap was highly elemental in pursing this and thank you very much for your attention. [applause] Male Speaker:
All right, great work. A couple of questions. When you looked at your K27ac chip and there
were two conditions, you only had about 50 sites of overlap. When you had a GR chip and
the two conditions, you had over 200 sites of overlap. Do you think there was an issue
with the K27 chip? It’s not like it’s efficient? We need to look at other marks? Or you think
there’s a bunch of GR sites that are silent, even though they’re recruited? Evan Rosen:
Yeah, it’s a great question. So, there were about 200 and some odd sites that were together
there. So some sites will get recruited and not change their K27ac very much. Maybe because
they’re trading factors, maybe GR is going on and something else is going off and the
overall activity enhancer as measured by K27ac is not changing. That’s one possibility. These
are blunt tools. I think that’s also a part of it as well. so we don’t tend to worry about
the stuff we don’t see as much as try to make use of the stuff that we do see and that’s
a little bit of a cop out answer but that is how we deal with that. Male Speaker:
I agree. The second question, how are you taking the genetic heterogeneity into account
in the second part of your study, the human focused samples? Evan Rosen:
Yeah, so we are doing that right now. So this — we are integrating the human genetic variants
now, so I don’t have anything if that’s what you’re asking. Male Speaker:
But from the samples from the individuals you’re getting, are you assessing what’s their
identify? You’re chip sequencing; are you sequencing longer reach to actually be able
to ascribe reach from one of the old versus another if indeed your patient is — Evan Rosen:
Yeah, yeah. So, in the early samples we were not actually consented to get genotype data
from those, so we’ve been doing a little bit with imputation with that, but in the — all
the later samples, we have changed our consent form and so we’re getting — to get that information.
And we’re going to just SNP genotype everybody and just figure out, to get that data. Female Speaker:
I have question too. Does — are there adipocyte stem cells? Evan Rosen:
Yes. Female Speaker:
And do you separate them and analyze them? Evan Rosen:
So it’s a very contentious area of research — what exactly is an adipocyte stem cell?
They do exist and they come, in our human studies — they’re not in the cultured cell
studies. The pre-adipocytes that are used in the culture studies are from a later stage
of differentiation. They’re not stemmy. They sort of have lost all multi-potency and can
only become adipocytes. But in the human studies that gets fractionated out. Remember we collagenate
and when we float off the mature adipocytes and all the stuff goes down to the bottom
which includes immune cells, stem cells, pre-adipocytes, epithelial cells, neurons, anything else that’s
in there is in that. So that is a separate area of investigation and flowing out the
specific stem cells is something — we save those fractions and we’d like to do that,
but the field I would say is in disarray about what — it’s not really our area of research
constitutes an adipose stem cell. So we’re not sure what markers to use to flow out the
actual stem cells. Okay. Thank you. [applause] [end of transcript]

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