Aviv Regev: Yes. So thank you for the opportunity to speak
today, and to try and pitch to you the human cell atlas. So why cells? Well, cells are
our core unit of each and every one of us, actually of any living organism on earth.
And our cells come in many different flavors that we like to classify into different types
by different types of characteristics; their function, their shape, more recently their
molecular profile. So based on this for example might distinguish in the immune system, dendritic
cells that are our first line of defense, from T cells and adaptive immunity, we can
distinguish neurons from glial cells like astrocytes and so on.
Now, cells are also a key intermediate in between the genotype and the phenotypes that
we care about so dearly. So for example, a genetic variant manifesting in immune dendritic
cells could increase the risk on Crohn's disease. Another genetic variant manifesting in skeletal
muscle cells could end up in muscular dystrophy and so on. So it becomes completely obvious
that if we actually want to functionally characterize variants and act upon them we have to know
which cells we are dealing with. So what would be incredibly handy is if we
had some sort of catalog or atlas of all cells. But what exactly would be in this kind of
atlas? Well, obviously the first thing that should be there is the listing of all of the
different cell types so we want to have our neurons and out different immune cells, and
so on. We also want to be able to distinguish between different states for the same cell
type. For example, an immune cell that is completely naïve compared to the same immune
cell type after it had encountered a bacterium. We want to be able to position all of these
cells in their three dimensional locations. We want to be able to identify the salient
features as these cells undergo dynamic transitions, for example during differentiation or when
they’re activated. And finally in an ideal world we'll also be able to trace their entire
lineage as people can easily do for the worm C. elegans.
I think it's very fair to say that each and every one of these five aspects our knowledge
about it is limited, some less, some more. It's also very rarely informed by genomic
information. It’s mostly done by pre-genomic tools. Some of it is, you know, years old,
a lot of it is decades old, and some of it is actually, you know, centuries old.
But very recently, advances in single cell genomics all of a sudden make a much more
systematic catalog within reach. And this is because, first of all, we have new technologies,
molecular biology and the volume of sequencing that is needed in order to measure the transcriptions
of single cells with great precision. We also have sample preparation methods including
the ability to isolate cells on multi-well plates and handle them with liquid handlers
and microfluidics that let us process a lot of cells in the right scale and for the right
cost. And finally, we have rapidly maturing computational
methods for everything from QC to cell type classification to identifying markers. And
as a result even though single cell genomics is very young, it’s already taking on very
quickly across the broadest scientific community. And just based on numbers from the Broad [spelled
phonetically] where I'm at, if at May 2012 we had 18 cells profiled, then by now we have
profiled about 100,000 cells. Now very importantly we're also making very
quick advances in critical additional capabilities that will bring us the right scale in resolution.
So for example -- hopefully this works, yes -- so for example, using reverse homologen
droplets, which is what we see in this movie, we can handle cells very quickly, at the rate
of thousands per second and with clever bar coding we ca bring down the cost of sample
prep below three cents a cell. In addition advances from many different groups
are showing that we can measure many other variables besides RNA in single cells. And
finally, other advances are able to use experimental methods to likely register cells to 2D or
3D position. Now, these advances are already quickly leading
to new insights. So here for example we are seeing profiles from 800 -- roughly 800 dendritic
cells isolated from the blood of an Asian-American 24 year-old female. The vast majority of these
cells allow us to identify completely from scratch all the foreign known subtypes of
dendritic cells in the blood. But there is also a small minority population, under 2
percent or so, that is quite distinct and can be identified with a unique signature.
These cells are only 2 percent of dendritic cells they're only 0.06 percent of peripheral
blood mono nuclear cells. But they are validated readily in 10 of 10 independent individuals.
And looking at these profiles we can actually infer their position in the lineage, and identify
them as a likely new blood precursor of human dendritic cells.
Now this is one example of an insight around lineage. We can also identify new neuronal
subtypes. We can find new states for well-known types like pathogenic T cells, in models of
autoimmunity cell nucleus blastoma with different states. We can monitor cells during dynamic
transitions in very quick immune responses, or in longer differentiation processes, in
diposite [spelled phonetically] cells. And we can even by coupling single cell RNA-Seq
of this aggregated cells from basically a piece of tissue together with clever informatics--
again, let me try to run these for you, we can actually map these cells back to their
three-dimensional positions which is actually what we see in these rotating spheres that
represent the zebra fish embryo with about the 10,000 cell stage.
So this all suggests that the sequencer may very well become the microscope for the 21st
century. And what I want to do in my other five minutes is to try and suggest how we
could put this microscope for very good use in order to build ourselves a human cell atlas
that gives us all of the types, states, transitions, and locations of cells.
But first, let me remind you why we would actually want such and atlas. And this is
because this kind of atlas will be a reference map for all functional studies. If will provide
us foundational fundamental knowledge across all biological systems. It will allow us to
characterize the function of genetic variance, which is what this community cares about deeply
in the relevant cell type. It will allow us to interpret pathology and heterogeneity in
disease with respect to an actual reference of normal. And it should eventually allow
us to characterize this kind of heterogeneity in individual patients.
I also want to claim that this can only be done within the context of one of those large
scale manage consortium type technology advancing projects -- all of these underlying terms
are actually from Eric Green's presentation earlier today, that the NHGRI excels at, and
really shows the way at, compared to other institutes.
So first of all let me say a few words about how. It’s a unified project I probably don't
have to sell you so much on the idea that it's large. And because it is so large I suggest
that we should probably first engage in a pilot project in a few complimentary systems.
Maybe the blood, which is easily accessible but heterogeneous. Maybe the gut which has
a lot of different type of cells, immunity, epithelial, neural, others, all embedded in
a three-dimensional tissue. Maybe the liver. Obviously we need to do it in partnership
with the expert communities and the relevant institutes. We want to do it with a standardized
controlled process. And by developing and deploying shared analytical tools. And finally
we could realistically set ourselves the goal of driving the cost per cell to about 15 cents.
And this is already quite realistic with the ability of using these droplets and with the
yield of a high Seq 10X. And if sequencing cost drops even further then this number can
go substantially lower. Why do we want to do it as a big large consortium-managed
project? Well, because in this case there are fundamental scientific and impact related
reasons that for which only a large scale project makes sense. So first of all from
a scientific perspective only if we do it in a standardized way from sampling acquisition,
to sample prep, to QC, to analytics would we ever have a chance of actually creating
a meaningful catalog that is not riddled by noise. And our body didn’t hear that it
actually belongs to different institutes. So almost in any system that you would isolate,
you would find cells that belong to more than one.
Now, scale serves a lot of purposes. One of them is obviously to drive the cost down and
that's very important, but only under the umbrella of scale do you usually actually
garner the energy and the focus needed in order to have those technology advancing things.
In our case novel approaches to sample prep and to cell isolation and analytical tools
and there are the things that eventually will be served to our community as resources to
the entire community of biomedical researchers and should be commensurate with the clinic.
And then finally, because obviously this is a large project, I want to spend half a minute
on numbers. So you might not know, but the human adult consists of about 20 trillion
cells when you don’t count the third of us that is actually red blood cells that fortunately
don’t have a nucleus so we don't have to worry about them.
That's a big number. With 50 million dollars, that's what I gave myself instead of 100 or
at least 50 million that we can spend directly on the cell that gives us maybe 500 million
cells at 10 cents a cell. That's only 10 to the minus fifth of the -- 10 to the minus
five of a human. That sounds a little low balling.
But there are some encouraging things, like the fact that we know there are about 300
major cell types. But then again, if you consider something like the retina, which is one of
the best characterized systems for cell types, it might consist of three of the 300, but
it actually has 100 sub-subtypes inside them. So it's defiantly a big project. However,
it's also a finite project. And what is very critical is the fact that in order to build
an atlas, we don't have to measure every single cell. In the same sense that when we chart
the map we actually don’t do it in a one-to-one scale, we do in it a one-to-one hundred, or
one-to-one thousand, the level of resolution that is needed. And with simple calculations
that we can show we can sample orders of magnitude less cells and get the right representation
for an appropriate survey. So in the example of the retina where there
are 150 million neurons in the retina, given its complexity which is probably unsurpassed
in biology, only 40 thousand would be required for a sufficient survey.
So I pose it to you that 10 to the minus five of the human body would not be so bad if done
judiciously. And could give us a very good resolution of an atlas that the need is enormous,
that the tools are in place, and the goal is within reach, so we should do it. Thank
you very much. [applause]
Male Speaker: Any questions?
Aviv Regev: Ewan.
Ewan Birney: Very cool. So in that last bit where you were
looking at the kind of the numbers, you didn't put the state expansion, the combinatorics
[spelled phonetically] with state, and I mean, my sense is that these spaces are sort of
complex and fractal and I mean, in other words they're sort of bound-able, but if one wanted
to get more resolution one could always get more resolution and the question is at what
point are these things useful? And they're useful I think very early on, I mean, before
one even gets to the scales that you're suggesting. So my one comment there is about state, the
second comment is combining this with microscopy, so this is a kind of a new microscope, I buy
that, but I know that there are many pushes and drive into imaging and microscopy at a
variety of different levels, and especially at the-- I mean, you have this on the top
left there as well, but I think integrating this kind of process, especially the model
organisms which are much more tractable, with high end organismal microscopy would be incredibly
powerful. Aviv Regev:
I will answer both. The first is about state. Yeah, state can be a tough one, but state
probably will be saved by the modularity of the systems that we study, so, yes you could
envision an endless number of stimuli that would lead to an endless number of variations,
what I think you refer to as a fractal, but our experience in those places where people
have tried to jiggle biological systems with these types of continuous responses is yes
that there were variations, but things were not endlessly variable in terms of the response.
That’s maybe because of canonization, maybe because of functionality. In transitions this
could be a lot more difficult but I would say that it would probably beyond the scope
of the first phase of a project like this. I think in states we could go after some,
we couldn’t go after everything because we probably don't know all of the necessary
stimuli. In terms of the relation to microscopy I think
it actually goes very nicely hand-in-hand. I think that there is a number of technologies,
this one is based on computational mapping using the crudest in C2 data out there. Straight
images out of paper is on the fish. There are finer methods that can rely on finer images,
and there are also emerging technologies in the single cell proteomics that actually couples
slices straight to readout and I think would be adapted for the RNA domain as well.
Ewan Birney: Sorry, just to add to that I've been really
impressed by these light sheet approaches, I don’t know if you’ve seen these light
sheet approaches but the resolution they can sample at is just incredible --
Aviv Regev: Yeah. There are phenomenal opportunities there.
Given the 10 minutes I just put one, cute figure. Ross.
Male Speaker: Now that I have the chance to study the slide
a little more I see you've already thought of this very obvious thing, but I want to
give you the opportunity to expand on it, this should be done in-- I love the idea by
the way, I would do mouse and parallel. I mean, not wait, but go right ahead. I think
there's a lot of richness that could come from looking at mouse and then helping us
interpret, you know, to study some mouse and how to extrapolate that to human.
Aviv Regev: I think that's a phenomenal suggestion to
do it hand-in-hand with the probably the most prominent model organism of mammalian biology.
I think it is completely doable, it would probably also resolve a lot of arguments that
I don't have a strongly held opinion one way or another but that it would at the very least
tell us where we can go to the mouse as out model and where we cannot. Thank you. Yes.
Male Speaker: I just have a couple of pedantic questions
-- Aviv Regev;
Go. Male Speaker:
One is I noticed on your slide on your cost estimates you suggested something --
Aviv Regev: It's somewhere --
Male Speaker: like 10 to the minus fifth [unintelligible]
or something per cell. Aviv Regev:
Yeah.
Male Speaker: And just wanted to get a feeling from you
about how thorough you think that would be. And second of all, when you think about the
microfluidics solution that you had for lots of cells you mentioned you've done upwards
of 100 thousand cells, is that system ready for prime time yet?
Aviv Regev: Okay, let me answer both questions. The first
was about the cost in the 100 case cell reads per cell. I think the number of reads per
cell actually depends not just on type of resolution that you want to have but on the
particular system. And we have pretty good ideas from the biological literature when
you have to go deep and when you do not. There is a nice paper earlier in Science this year
Jetten et al. that shows for broad immune classifications you can very well do with
a thousand reads per cell. I went higher end because if you talk to a
neuroscientist about distinction for example, in the retina, which are incredibly important
and functionally distinct entities these neural sub-subtypes, actually the defining characteristics
are at a much lower level of expression than the things that typically classify immune
cells, one relative to the other. The current molecular biology of single cell RNA-Seq the
conversion rates that actually don’t really vary from protocol to protocol really say
that above 500 thousand cells all you are really doing is sequencing duplicate reads,
so that doesn’t help, and it seems to us from our own analysis I don't speak for my
own lab only but for the community of people who works with single cell RNA-Seq data that
you get the best bang for your buck from the first hundred thousand and then its diminishing
returns. So that's our current knowledge. If our sequencing
were a lot better we should go with more reads, but were just not there. If our sequencing
for molecular biology was better. About the drops. No, the 100,000 cells were done by
a mixture. Some of them with microfluidics, some of them with regular plates followed
by automated robot, you know, liquid handling, and some of them were first done, you know,
by hand, but nobody does them by hand anymore. The revers immersion droplets I would not
put on the slide if we didn’t have really encouraging preliminary results. We think
we turned the corner on them, but we haven’t used them for the 100,000 count.
Ewan Birney: One more question.
Aviv Regev: Yes.
Ewan Birney: Is there a strategy to advance the lineage
map ahead of the other assessments, or is it the concept that you have to know the states
and the locations before you can do the lineage map?
Aviv Regev: No, actually you don’t have to know the
states nor the locations to do the lineage-ing according to the people who do lineage-ing.
There are several strategies, most of them are based on judicious genome sequencing,
not whole genome sequencing, and there doesn't seem to be an impediment to combining that
with RNA readout of the state of the cell, which mean that you should be able to infer
lineage. Having said that, it would require work, I would say, that if I had to say off
the top of my head I would say that the detective for that and very early proof of concept would
be appropriate for phase one, but not the full application.
Male Speaker: Thanks very much Aviv, we have to move on.
[end of transcript]