 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 units of each of every one of us actually of any or living organism on earth and Our cells come in many different favors Flavors that we like to classify into different types by different types of characteristics Their function their shape more recently their molecular profiles So based on this for example might distinguish in the immune system dendritic cells that are first line of defense from T cells in adaptive Immunity we can distinguish neurons from real 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 of 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're 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 a listing of all of the different cell types So we want to have our neurons and our different immune cells and so on We also want to be able to distinguish different states for the same cell type For example an immune cell that is completely naive 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 location 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 will also be able to trace their entire lineage as people can easily do for the I know the worms see elegance 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 this knowledge 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 both molecular biology and the volume of sequencing That is needed in order to measure the transcriptomes 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 with microfluidics That lets us process a lot of sale sales 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 where I'm at If at May 2012 we had 18 cells profiled then by now we have profiled about a hundred thousand cells Now very importantly we're also making very quick advances in critical additional capabilities that will bring us the right scale and resolution So for example Hopefully this works. Yes. So for example using reverse homologian 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 barcoding We can bring down the costs 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 single cells and Finally other advances are able to use experimental methods to likely register cells to 2d or 3d positions Now these advances are already Quickly leading to new insights. So here for example, we're 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 of the foreign known sub types of dendritic cells in the blood But there's also a small minority Population under 2% or so that is quite distinct and can be identified with a unique signature These cells are only 2% of dendritic cells They're only 0.06 percent of peripheral blood mononuclear cells But they are validated readily in 10 of 10 independent individuals and looking at this 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 sub types We can find new states for well-known types like pathogenic T cells in models of autoimmunity Cells include Blastoma with different states. We can monitor cells during dynamic transitions in Very quick immune responses or in longer differentiation processes in adipocyte cells and We can even by coupling single cell RNA stick of disaggregated cells from basically a piece of tissue Together with clever informatics We can actually map these cells back Map these cell back to their three-dimensional position Which is actually what we see in these rotating spheres that represent the zebra fish embryo at about a 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 can 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 an atlas And this is because this kind of atlas will be a reference map for all functional studies It will provide us foundational fundamental knowledge across all biological systems It would allow us to characterize the function of genetic variants 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 project all of these underlying terms are actually from Eric Green's presentation earlier today that the NHGRI Excels at and really shows the way out 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 that has a lot of many different types of cells immune 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 Institute We want to do it with a standardized controlled process and by developing and deploying shared analytical tools and finally We could set ourselves realistically 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 than 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 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 sample 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 will 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 these are the things that eventually will serve 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 a half a minute on numbers So you might not know but a 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 that's what I gave myself instead of a hundred or at least 50 million that we can spend directly on the cell That gives us maybe 500 million cells a 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 bully But there are some encouraging things like the fact that we know that 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 three hundred, but it actually has a hundred sub sub types inside them So it's definitely 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 a map We actually don't do it in a one-to-one scale We do it in a one-to-a hundred or a one-to-a thousand the level of resolution that is needed And with simple calculations with the can show that 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 40,000 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 Ewan Very cool. So in that last bit where you were looking at the kind of the numbers You you didn't put the state expansion the combinatorics with state and I mean my sense is that these spaces are sort of Complex and fractal. I mean in other words, they're sort of bounder bowl But if one wanted to get more resolution one could always get more resolution The question is is what what point of these things useful and and they're useful That they're useful. I think very early on. I mean even before one gets to the the scales that you're Suggesting So I say anyway one comment there is about state. The second comment is Combining this with microscopy. You said this is the kind of a new microscope I buy that but I know that there are many pushes and drives 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 in the model organisms, which are much more tractable with high-end Organismal microscopy would be incredibly powerful. I will answer both the first is about state. Yes, they can be a tough one But state probably we 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 That's 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 they were variations, but things were not endlessly variable in terms of the response That's maybe because of canalization. Maybe because of functionality in transitions This could be a lot more difficult, but I would say that they are 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 actually think it 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-situ data out there Straight images out of papers on the fish. There are finer methods that can you rely on finer images? And there are also emerging technologies in the single-cell proteomics that actually Couples couple slices straight to read out and I think would be adapted for the RNA domain as well So just just out there. I've been really impressed by these light sheet approaches I don't know if you've seen these light sheet approaches, but they are they are The way that the resolution they can stop that is just yeah There are phenomenal opportunities there given the ten minutes. I just put one cute figure Ross Now that I have a chance to study the slide a little more I see you've already thought of the this very obvious thing, but I want to give you the opportunity to expand on it We should this should be done in mouth. I love the idea by the way and I Would do mouse in 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 in helping us interpret You know Studies in mouse and how to extrapolate that to human I think that's a phenomenal suggestion to do it hand-in-hand with the probably 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 it would it the very least tell us where we can go to the mouse as our model and where we cannot Thank you. Yes I just have a couple of pedantic questions which is one is you I Noticed on your slide you see you on your cost estimate She suggested something like 10 to the fifth reads or something per cell Yeah, and just wanted to get a feeling for you about how thorough you think that would be and second of all When you think about the micro fluidic solution that you had for lots of cells You mentioned you've done upwards of a hundred thousand cells. Is that system ready for prime time yet? Okay, let me answer both questions. The first was about the cost and the hundred case cell reads per cell. I Think the number of reads per cell actually depends not just on the 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 JT Natal that shows that 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 distinctions for example in the retina which are incredibly important and functionally Distinct entities these neural sub sub types 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 seek the current conversion rates that actually don't really vary from protocol to protocol Really say that above five hundred thousand cells all you're doing is sequencing duplicate reads So that doesn't help and it seems to us from our own analysis And I don't speak for my own lab only but for the community of people who works with single cell RNA seek data That you get the most bang for your buck from the first hundred thousand and then it's diminishing returns So that's our current knowledge if our sequencing were a lot were better We should go with more reads, but we're just not there It's not sequencing for molecular biology was bad about the drops. No the hundred thousand cells were done by a mixture of 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 reverse emulsion 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 hundred thousand count One more question. Yes. Is there a strategy to advance the lineage map ahead of the other Assessments or is the concept that you have to know the states and the location before you can do the No, actually, you don't have to know the states nor the locations to do linear gene according to the people who do linear gene 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 inherent impediment to combining that with RNA readout of the state of the cell Which means that you should be able to infer lineage Having said that It would require a work. I would say that if I had to say off the top of my head I would say that the tech dev for that and very early proof of concept would be Appropriate for a phase one, but not the full application Thanks very much. I've even had to move on