Howard Chang (Stanford, HHMI) 1: Epigenomic Technologies
I'm. Howard Chang a professor. In the Stanford. University in, California an investigator, of the Howard Hughes Medical Institute, today. I'll be talking to you in, three-part, talks about, epigenomics. And long, non-coding RNAs. Epigenetics. Is a very hot topic today, the. Word literally. Means above, the genes and you, can remember, the catchphrase that, your DNA is not your destiny and a very. Good example of this is that nearly every cell in your body has. The same DNA yet. Your skin cell is not the same as your muscle cell or your, brain cell and that. Is because these cells have choices, choices. About which genes to turn on and off, and. This. Comprehensive. Study of these, gene Beckett or events is the, modern study of epigenomics. Literally. We can think about epigenomics. As studying. The living genome, this. Feel has evolved, so that we can out directly, measure these activities, but. It has important, implication, because it has the dynamics, of the interaction between nature. And nurture, or, you're born with and the impact of your environment, as. Such this, may have important, implications for, your personalized, health for. Example in clinical, applications, and also monitoring, health States. Here's. Another potentially. Useful analogy to think about the, relationship, between your DNA, your genome, your, epigenome. And the. Involvement in potential, disease states, we. Can't match in that your genes are like, this template, information, like this image and the, epigenome, as the lens through, which of the information, is projected, to, show this beautiful image. With. Aging, in or with disease this. Template, get degraded, and the lens. May become cloudy so, this image is now blurred. The. Promise, of epigenetics. Is that, perhaps we can actually fix the situation. Even. If the genome, information. Is still somewhat, degraded, the, lens. The, epigenome, through which is information projects, can, be corrected, and in, such a way like the glass I'm wearing to actually restore, this image and basically, restore, the. Phenotype. We associate with a healthful state that. Is the conceptual, promise. Another. Reason for the excitement, for epigenomics, is because the technology, is really at an inflection point in. Every, field technologies. Go through different, phases of. Discovery. Detection. In systematic, decoding, in the. Field genomics, the hardware. The, DNA in ourselves, we. Discovered. The, structure of DNA in the 1950s. The. First technology to detect or sequence, DNA. Occur. In the 70s, but, only in the last decade. Or so that we have really next-generation, sequencing. Technology, to make routine. Genome. Sequencing, a possibility. Epigenomics. Is also, going through relate. A kind of, evolution. And we, can think about the epigenomics. Now as the counterpart. The software, programming. Of our cells so. The first chemical Bark's, associated. With, epigenetic. Memory were discovered in the 50s, some, of the first method, to detect these. Marks, in a laboratory setting or, develop, in the 80s and, I'll be telling about some new technology that, developed were developing the last decade, I really, sped up the capacity, to systematically, code this epigenomic, information. Let's. Zoom, in to the specific, features, in the genome that we're talking about when. We think about genes, specifically. Disease associated, genes we. Have to remember that each of these genes are associated with, switches. DNA. Regulatory, elements, that decide when, and where this, gene turns on and, off these. DNA. Elements are the binding sites for, transacting. Protein. Transcription, factors, or regulatory, RNAs, the. Picture in the human genome actually look more like the bottom where own just 2%, of information, is protein, coding and the, vast amount. Or the real estate 98%. Is actually, part, of this regulatory DNA we. Also know that that human. Variance associated, with disease reside. In this non-coding, space. So. Systemic, work over the last several. Decades by, many investigators, have, found that Dulles, DNA is packed into chromatin, and I'll, refer you to the eye biology talk by David Allis which goes into great depth about these different chemical, marks but, the in conclusion, is really, that each part of the gene has, characteristic. Chemical, and physical features, on chromatin, and that. These features, reflect, the current activity, and a future trajectory of, the genes and looking. And epic, genomic technologies, I'm talking, about basically. Is, the systematic, mapping of these chromatin features across, the genome so.
This Cartoon shows the fact that if, there's a protein, coding gene here that, there be promoters, that's where the gene starts there. Are DNA elements like enhancers, that activate. This gene in specific, cell types, there. Are additional DNA. Elements that might prevent, the shin from being activated in the different situation, and there, also for example insulators. Things, that basically break, up genomes, into neighborhoods, of control. And, this. Interaction, would, have to occur of presidential, through long-range, DNA looping chromosome, looping a very. Fundamental feature, of these activities. Is, that, the DNA, has to be accessed, it has to be physically, touching, the regulatory she nuri for, this regulation to happen and that is, a fundamental. Feature that, we can exploit. Now. In every human, cell two. Meters of DNA is, packed into a 10 micron nucleus. Therefore. Most of your DNA is highly compacted. All wound, up and not accessible. Except. That the act of DNA elements that. Your cell is actually, using, and reading and so. Simply finding, out where, these accessible. Elements, are located. Jacob. Can give us a lot of information about. The software program, that your cell is running a few. Years ago my colleague, will Greenleaf ania Stanford, invented. A technology, called a Co transposes. Accessible chromatin, or a, taxi. For short, it, uses, an enzyme. Called. T and Phi transpose it which copies, and pastes DNA, it derived. From a bacteriophage. We. Already load up this enzyme with, sequences, that can go onto our sequencing, machine. When. This enzyme, tries to copy and paste into. Eukaryotic. Chromatin, it can only paste, into the open chromatin science and so. Therefore in a single, step you selectively. Incrementally. Tag, the genome at. The accessible, sites that, then allows us to amplify and sequence these elements, so. This a very, elegant simple, sort, of strategy, led. To a million, fold improvement, in the sensitivity and a hundred full improving the speed of mapping. The regulatory DNA the, epigenome in, human cells. Here's. The example of what the data will look like on, the x-axis, these, are the locations, of genes and the. Height of these Peaks indicates. A level accessibility. The taller the peak the more accessible it is the, first track show in blue was the standard, prior. Gold, standard technology, called, DNA's hypersensitivity and. They, used ten million cells the. Second, row in green is the. First virtual attack seek technology, which you only used, 50,000, cells and the. Third road with the ultimate resolution that we achieved which, was actually single. Cell attack, seek this is several, hundred single, cells some, together you can see that the patterns look very, similar, across, these applications.
However. Now. With the single saw information, you can zoom in and now. Every. Row is a single cell your 254. Single cells going down this way and at, every position, here. Every. You. Basically, see either 0, 1 or, 2 reads because, human, cells are diploid and therefore. This kind of analog, signal, can turn into a channel information. When. We see these individual, Peaks we, of course want to know what are the factors, that are acting, on this individual, or gene switches. There's another very interesting feature of, the attack seek signal, that we can exploit we. Learn that, many times at the. Summit, of every peak there's, an approximately. Eight to ten basis, of a dip and. This is called a footprint, okay so this is an example of an attack seek signal and this is exactly the binding side of a, DNA binding factor, on to. DNA, so. The idea is that we're essentially, spray-painting. The genome with, our a toxic, enzyme, and if, a, protein, sitting on the DNA you can spray paint to the left of red or to the right of it but not on top of it and so. If I were putting my hand in front of a wall and I spray painted when I move my hand away you'll see a shadow it shows that an object, was there that, is the kind of similar principle, and so, we can see that if we're directly, retrieve, this particular, factor, call, ctcf. This, is the location where the CDC F is sitting and the, footprint, of the ctcf. On a, taxi, data looks, very similar. Okay. So, because we naturally know the, binding, preference, of hundreds. Of factors across a genome, we can actually look across a genome, and ask. Where, do we see this kind of footprint and infer, the, binding, locations, so, for example I look, in this map, here, every. Call every row is an, instance, of the ctcf, binding site, in the genome this. Is the center of the sequence that it's being bound to we, can see that only these sites up here, have. This kind, of footprint pattern and these ones at the bottom do not if. We directly retrieve. Ctcf. By a different, technology we. Can see the same answer that these top ones are bound and the bottom ones are not bound okay, and so, because again that, we know the, binding sequence or the preference, of hundreds, of factors in. That, bound to DNA we, can actually learn the binding locations, that these factors are once in allele, specific fashion. Now. That we have this powerful technology, we, can think about what we can learn from this, map, this epigenomic, map of individual. Cells and, I. Analogy. We were really quite inspired, by the kind of maps digital, maps that, we all use in our daily lives or navigation. These. Digital maps represent, the real world in different layers including. The, lay of the land the, different. Businesses. Different streets, where, your friends are and each, of these layers of the map makes, this map a more useful, so. We also imagine, that by analogy. Now. If we build up a personal. Snapshot. Of gene, regulation the epigenome of the regula we, want to learn from this map different, cell types different. Cell states the, tissue micro environment, the, cell lineages, the, effects of perturbations. Or drugs connect. Them all together from computation. And to, kind of they make maximal, use of, this personal. Regular, information. I'll. Show you some examples how can extract this kind of information, from, the. Epigenomic, map. An. Important concept is that the epigenome. Encoding, information or cell type identity, here. On this map I'm showing. You six. Tracks, six, different cell types from the blood starting. From the hematopoietic, stem cell, the HSC. Two, cells that make different lineages, myeloid. Cells white cells read. Us mep. Which makes red cells and specific. Kinds of immune cells cd8. T-cells and NK cells on the. Right you can see that for, this particular gene, tattoo. The messenger, RNA level, varies.
By Less than two fold across these different cell types so, you might think that tattoo, is not a very good marker for different, cell type identities, but. If you look at the chromatin, landscape, now, you see a completely, different picture which. Is shown on the, left so, you can see that the tattoo, promoter, it's, accessible, in all these different cell types but, didn't you see that progenitor, cells, have, one set of accessible elements, and further. Elements, distinguish, let's, say lymphoid. Cells and specific. Kinds of cells. Are just see these eight cells and NK cells, so. The message here is that each, of these cell, types they're, making the same turning. On the same gene making, the same RNA. I did doing it with different gene switches, and these, switches then tell us the identity of cells that are involved. This. Particular concept, can be particularly powerful when. We think about the problem of cancer, cancer. Cells are individually. Different and this. Has been long, known on, the. Left is a play from a paper, by Virchow. Back, in 1847. It, was drawing, images, that. He saw under the microscope you, can see that individual, cells are not all identical, and. The right hand side is a more, modern image. From a review which raises. The concept, that tumor, cells can go through this kind of epigenome. Make a chromatin, changes, and that changes their behavior. We. Use our technology and, teamed up with colleague, at Stanford University, to study human. Leukemias. Acute, myeloid, leukemia in. This particular disease, which is a cancer of the blood cells, we, know that the hematopoietic, stem, cell, the. H has C that gives rise to all the other cells in the blood suppers. Are series of mutations, their, first mutation and create, something called a. PH. SC, and further. Mutations, causes, a cell, shown yellow here that leukemic, stem cell that can now propagate, the disease there's, still a minority of cells in the blood the, vast majority of cells are these blast cells which, is color red here we're. Able to isolate all C different cell types from, leukemic, patients and show that in fact they're also in parallel, to the genetic, changes, there's, corresponding. Systemic, changes to the epigenome, as, these cancer cells progress through, these different as stem cell fates. We. Can also answer some important, questions we. Know that in certain cancers. In leukemias, that the, leukemic cells will show features. Or, markers, of different, kinds of normal. Cell parts cell, types, and so, this is a confusing. Situation, people. Are not sure whether it's because there are two kinds of cells running.
Around In leukemia or, is it that really there's one cell running, two programs, and, so. We use our single cell technology. Single cell attacks each to. Examine, a. Leukemia. Patient, in this case a patient, so he, makes themselves so. On the graph on the right here, each. Doc, shows, either individual, cells or particular cell type and this. Two-dimensional. Plot here indicates. Our relationship, by distance, the. More related. The cells are the closer they're together and if they're far apart amines are quite different well. What we see is that these purple, cells two individual, cells from, the cancer patients. They do not map to any of the known cell types they map in between them and that really indicates, that it's a single cell running, two, different programs a concept, called lineage, infidelity. It. Also further turns out that, the more that these cancer. Stem, cells are running. The program the normal hematopoietic, stem cell to HSC the, more they able to copy themselves and renew, themselves and that, in that case of cancer is a bad situation. We. Found that in fact that. There's. Quemic. Stem cells with a high sort, of phsc, potential. They're, much, more likely to cause death. Unfortunately. For the patients whereas, those have with a low THC, content, have. A much better outcome and so. Therefore, we can see that even this epigenome, information, has potential, prognostic. Information. We. Were able to extend these concept, into, also solid cancers. Cancer. Genome Atlas has. Been a major effort for the cancer community over. The last decade, and many. Investigators, have systematically, collected. Nearly. 10,000. Tumor samples, and sequence. Their, genome, sequence, their RNA but until very recently we didn't have any any information, on the, epigenome. Landscape. We. Teamed up with. The TCGA, group and we really use a taxi, technology, to map the. Chromatin. Landscape, in 23. Human cancer types which, are shown here on the, right and these span some of the most common, and deadly human cancers including, glioblastoma. Lung. Cancer breast cancer. Colon, cancer and so on and so forth we. Studied 410. Tumors, and we discovered over, half a million DNA, elements that are active, in these diverse cancer, types, what. Is very intriguing is that we found that nearly half of these elements are not, active, in. Our surveys, or normal, tissues, they're, only activated. In the, contacts, in the pathology of, cancer. We. Can learn some really, intriguing, results. Geneticists. Have long studied different, families. Looking, at different risk of, cancer. And the. Vast majority of these, sort, of risks associated cancer, are actually. Following to the non-coding, elements and, so, if them it's a mystery as to how they might work so.
On The right is an image, coming, from the, epigenome mapping again. Genes. Are on the x-axis and, the. Hike on the y-axis indicates. Accessibility. At. The top in orange I'm showing you five. Examples, of colon cancer on the, bottom five, examples of kidney cancer. This. Gene being shown here is called, Mik is a very important, and powerful encouraging, and nearly, all the cancers return on Mick but, the point I want to make is that the, colon cancers, turn on Mick using, different elements show, more to the left side the five prime end of the locus and the kidney, cancers turn, on Mick with, a difference of elements more to the three prime end of the locus so again different switches. Even, for a common, encouraging across, different cancers the. Second important point is that, one. Of these switches, that's, turn on in colon cancer is precisely. The location for. Colon cancer predisposition, it's, only, active in colon cancer and conversely. An element. That's associated with, kidney cancer predisposition, it's. Actually only again, only turn on in kidney. Cancer okay. So, this, epigenome, mapping then, provided, us an ELISA, I think a biochemical, hypothesis. An explanation for. These, risk elements, associated with cancer predisposition. We. Also learned that beyond, inherited. Risk we can also explain, somatic. Mutations, those are acquired, in, the body in, the course of cancer this. Is a map, looking at a particular locus in. Different. Kinds of bladder cancers, and kidney cancers and we see that all these, cancers have the same landscape except, this one now all of a sudden gangs. This very strong. Accessible. Element. Activity, in, this, locus and. What we discover, there is that, if you look in the attack seek data. Well. This accessibility, comes from, a mutated, element and so. The normal sequence, is, shown at the bottom of this graph here, okay and the, mutated, sequence has changed a single base this, letter in T from. C to T, and, we'll, realize, here, is that the. Cancer, is, essentially. Hacking. The password, of the genome this. Sequence. Shown on the top here is the perfect, binding site for a particular transcription, factor called NK X and when. The cancer cell changes that C to, a T it now has the perfect binding site again for, NK acts and therefore, it gains as accessibility, because, the Machine starts, reading that part of the genome and turning, on the gene, we. Further found that the, gene linked, to this elements, called FG, d for when. FG d for level is quite high this. Is actually associated with a very strong, risk. Again, of of. Death and. Therefore. This is the kind of information, that, be quite valid too now we can therefore use the, epic unit information to understand both inherited. And acquire, Wisc of cancer. This. Technology, has continued. To undergo, evolution. In a very important recent advance is the increase in the scale of mapping. Single cell chromatin. Accessibility. This. Is using a micro, fula technology, that. Can parse individual. Nuclei, from. South into nano. Liter size drops, into. These droplets then, we, combined, them with. So, these are basic little beads that, contain, a DNA, sequences. Each bead, contains, a different sequence and that's the barcode and so, when individual, nucleus. Meets. An individual, barcode, we can transfer, the information from, the barcode, onto the nucleus, and that says that all the molecules in, that little drop came, from the same cell, once. We have tagged all these individual. Drops we can then break the drops and in sequence, all the, molecules. Together but. Then we should retain information that. They came originally from different cells so. This technology allowed, us to scale, up the the. Throughput of single, cell epigenomics, from, let's. Say several hundred cells, appart, a say to not tens of thousands, of cells, or perhaps even more in, the single experiment. We. Were able to recently, team. Up with colleagues. At Stanford University. To use this technology to look at a very important aspect of cancer, treatment, called, cancer, immunotherapy.
The. Poster, child cancer immunotherapy is, an, antibody, called, PD, one it's. Called checkpoint, blockade because, in releases. The, brakes that, are on the immune system for fighting against cancer and so. In this kind of work people are really interested in what. Kind of immune cells are coming in to fight cancer, and how do they change in the, progress. Of cancer, treatment, and. The, challenges, are that again we're, talking about clinical material biopsies. From patients that their timing, and you have one shot to get it right and. Because you can't just go back and keep, asking a person to do surgery, so. In the context, of a clinical trial for a kind, of cancer called basal cell carcinoma, we're able to see. Really biopsy. The same tumor, before, during. And after treatment and then subject, them to this very powerful single. Cell epigenome. Analysis, okay. So. In this map I call, a you map against, a two-dimensional, plot that represents, this, cell. Information, again. Related, cells are more clustered, together different. Cells are separated. And there, are nearly 30,000. Single. Tumor, infiltrating t-cells. In this map that we have analyzed. They've. Been color coded based on different classes of cells and the only point I want to make here is that this tumor microenvironment is. Really a world into itself it's really diverse and, there are all different kinds of cells that, you wouldn't have missed if you just average everything together into, a good mesh okay. What. We can further learn is that these cells are related, on, the Left I'm showing, you is trajectories. That we've mapped out based on the single sign attack see data of the. Cells as they develop so from naive CDA, cells into effector, T cells memory. Cells or exhausted, cells and also a knife cd4, cells into, these cd4, t fh cells but. What we learn on the right is that we can compare, the same patients, before and after, checkpoint, blockade and, ask what, populations, change and then, Brett really emerges, is that there's two populations. Exhausted. T, cells CDA. Cells and the cd4 positive, T FA shows these two arms, and going down and this is what expands, and we think are very important, for, cancer immunotherapy. We've. Been talking about individual, DNA elements and how can use that to learn about the epigenome it. Equally, important, challenge, is linking, these DNA elements to, their target genes and this, cartoon kind of illustrates part of the problem we, know that a gene regulatory landscape, is interweaved. A DNA. Element can actually control, a gene that's. Actually quite far away from itself there, might be several genes in between and therefore. Simply. Finding out an element's active is not enough to say which, nearby gene is actually, being controlled, and so. This is a question can phrase as the last mile of Human Genetics what. Is my target gene if, we got us a these large-scale studies find. DNA variants, our social disease now. We want to know what our that genes, under. Control that might be changed, and so, this. Really needs a different, aspect, of epigenome, technology, looking. Into DNA, folding, and how, does dean elements, touch their, target, genes.
And. So a technology. That, was developed that, we think is quite useful as, a method that, we, call the enhancer, connector, the. Idea is that we can take cells. Cross. Link them in, their native nucleus, to preserve the three-dimensional, contacts, we. Can then retrieve, the. Active, enhancers. Based. On one of these chemical, marks that I talked about in the beginning in this, case a histone, modification, called histone, h3 lysine, 27. Acetylene. And then. When you sequence, the these, contacts, what, you should get is a map like this where we can see individual, DNA elements in this case for example or, causal, variant for a disease and then, its target, genes in this case gene D and G a but. Not the nearest gene which, is gene, B I she. Mentioned that by default, in. The genetics literature people oftentimes, report, these disease, gene associations. Just, based on the nearest gene on the linear genome and this information may. Or may not be correct so it's really a shame that, we've done all this work but maybe haven't gotten the very precise information that. We need so. This enhancer. Connectome, method, actually. Proved to be quite powerful it, was a ten, thousandfold improvement, in the sensitivity, we, needed only fifty thousand cells instead of millions. Or tens of millions of cells and, there's also a tenfold improvement, in the sequencing, depth that one needs to get precise, information. Here's. An example of looking at a kind of rather rare, blood cell th17. Cells from. A human blood from an individual, standard, blood draw we, can see these, kind of sort. Of checkered. Maps. Relate. Long, range contacts, in DNA, it's. The same genome. On the X and the y axis, and therefore, anything that's off diagonal such as shown here of reflux or long-range contacts. And this, map to show that we can actually see this kind of contacts, from 500 kilobase resolution, all the way down to a kilobase resolution. This. Kind of mapping from. Primer, human cells is important, and needed, this technology, some. Of the rails, he analyzed, we, calculated, using. The prior technology, would, need about 4 litres of blood. Just, so everybody is on the same page an adult, human has, 5 litres of blood so, taking out 4 litres it's not something that I would recommend, okay so literally, would not be doable without, this kind of technology. Okay. So. Let's, just show first. Check. That informations. Accurate and, so, we're, looking again at this, very powerful Mik. On Corinne and this. Is something called a virtual, 4c, view. We. Have an anchor point which usually shown, by a dotted line and, that's. The point in the genome that we're looking from each, of these Peaks then would, be an active, enhancer, that is touching this.
Viewpoint And the, taller peak that means there's stronger, interaction, or is a stronger, enhancer, or, combination. Of both and so. This viewpoint told us that in this particular cell that we're studying this, Mik Jing is being contacted. And turned on by, these, Peaks these five peaks that are shown here so, how do we know that is correct, it turns out that a recent study by focal. At all and colleagues, they, actually went in and systematically. Try to block every, piece of DNA, in, this entire interval, okay, whether, it's known to be active or not and they found five, elements, show on the bottom here okay in red, hatch marks and they exactly, line up with, the locations. That were identified, by this enhancer. Connectome study showing, that information, is actually, accurate. Now, that we know that information perhaps useful we, can think about applying, it for something questions in human genetics for, example in this map of amine cells t-cells we. Know that there are DNA elements been, associated. By genome-wide, Association. Studies with diseases like type, 1 diabetes or rheumatoid arthritis so, what is the target gene the. Nearest, gene is this gene at the bottom shown the green called, SMI m20. It's not Gina has really any known relationship, for, demonology. But, in this enhancer, connectome, map we discover that, you, start from the viewpoint. This these diseases. Associate dean element, that, the true target genes are actually this gene are ppj. Which is very important for t-cell development and, a second gene called stem chew which, is a calcium, channel that's, involved in T cell activation and, that, makes much more sense, we. Can also verify that. The. These controls are really happening this. Is using a version of the CRISPR technology that. We used a dead cast nine to bring in a silencing, protein and this. Shows that if we target. Our PBJ promoter, we can silence, this lower. Its expression, and similar. If we target that disease associated element, that was predicted, to contact, our ppj, we also have equivalently. Powerful, effect in lowering expression, so, it shows that, in fact this. Element, disease, associate element is controlling. That target gene. We. Can expand, that concept and, ask systematically, for all these DNA, associated elements, in, let's, say autoimmune, disease what. Are the true target genes is there really the nearest, gene that's my report in literature, or could it be something else and in. Fact we, found that across either all autoimmune diseases, or, specific. Well known diseases like Crohn's disease multiple. Sclerosis. Lupus. Or type 1 diabetes that, there's nearly four, full expansion. Of, the protein, targets, or the genes, encoding protein targets by. A four-fold. Okay so a really substantial expansion. Of our understanding, of these, diverse disease, types. And. Finally. I want to talk to you about ways, of systematically, now testing, these. Sort, of nominated. Gene. Epigenome. Connections, and regulation, and that involves. Combining. Epigenome. Reading, with, epigenome, writing, and this, is a method that we've, called perturb, attack is a single, cell CRISPR, screen for epigenomic, phenotypes. The. Current, method for doing sort. Of large-scale CRISPR. Screens. Involves. Perturbing. A large population of, cells each. For example getting a different CRISPR, guide to, silence, or knock out a different gene we. Then impose some sort of selection, for, example cell growth or some sort of reporter, gene and we basically pull. Out a very, small subset of cells that met our criteria. We. Can sequence. The CRISPR guys and see which ones are enriched which, ones have been lost and we essentially, know what's been enriched so this is something and, so.
So We have these hits, but, everything, else that got perturbed, a didn't, sort of pass our solution gets lost okay. There, are many phenotypes that don't manifest, themselves as, cell, growth or reporters, in retail and so, the, concept of the perturb attack is that, we want to again perturb. Cells, lots, and lots of different combinations but for every single cell we're going to capture that cell we're. Going to sequence, the. Guide RNA and also, read out its epigenome, landscape, by a taxi. Okay, and this. Really means that we're doing, multi-omics. We're, recording two kinds two, modes of information. The chromatin, and RNA. To, make this possible and. So. This was accomplished, using, a microfluidic, platform. Where, we can capture the single cells and in different chambers first, perform attack seek then. Capture, the RNA, or. Code the molecules, from the same well and so we can then map this, single sound a taxi, to the single cell RNA information, and they'll. Graphs on the bottom show that this technology is actually working if we introduce a guide RNA for example, to this gene sp1, we, see a loss of accessibility, at xp1, and if, you look genome Y the targets, of sp1. Are also, being, impacted. We. Use this technology, to. Perturb, actually. Make lots, of different perturbations. Either. Singly, or in combination. So, here, every. Row. Is a different perturbation, and this, is a recording, of what kind of perturbations, have been made, then. We can see that, in fact their DNA regulatory, elements that get changed that are different with each perturbation. And, we can then show, on the third column what kind of factors, are most enriched, at the sites that, have been perturbed. And the results, we think make a lot of sense if, you could perturb this. Factor, called easy H to silence, aid this is an enzyme that writes, it's, a histone. Mark called k-27. H3k27. Trimethylation. Associated. With gene silencing so. If you get rid of easy h to the sites, that previously, had k-27, trimethylation are most effected and they're, all up regulated. Remove. The silencer, the, targets, get activated. If. We target a transcription, factor called sp1, okay, again this. Is a factor, that's involved in activating, genes so, the most affected elements. Are those that contain, sp1. Sites and you. Loose the activator, so the target genes go down so they're on the left side of this graph now. Finally, at the bottom this. Is a targeting. And, long coding RNA called eber to and prior, work has shown that it interacts, with a factor called pax 5 and indeed, a pax lies one of the most affected, a class of elements in, this particular screen. We. Wanted, to use the sun technology, to look again at the, disease, associated, risk in, there indogene oh and. We know that there are elements, that are affected I've showed. You how we can find them find their target genes but what we want to know now is that what do we have to do to. Affect, these switches, to turn them all on or off at the same time okay. And so, this technology the strategy we used in is to basically identifies, names autoimmune, diseases. Altered. These, regulators. In trance and asked which of these combinations or regulators can, most affect this disease, associate, elements and their nearby contacts, which we identify using, enhancer, connector and, so. This, is such a map is a very busy slide so every column, is a different disease, and we're looking at the DNA, elements that, are associated with that disease every. Row is a different, perturbation. Either, we're basically silencing. Different transcription, factors either singly, or in combination and, we want to ask which, of these factors, have. The most impact, selectively. On the thesis disease, associates. And then, the the. Color, coat, indicates the level of impact and just as, an example for this period disease called lupus what, we identify, is that among, these factors, that we examine, this, particular factor end of kb1, and coding NF kappa-b, the, p50, subunit, has, a strong. Impact and if we allow to do a combinatorial, studies, the second, factor shows up call relay now. It turns up encode the P 65, subunit, and a Kappa B and these two subunits actually, work together okay, so this unbiased, screen told, us that this heterodimeric. Complex, was perhaps very important, in, this particular disease as, a transacting. Regulator affecting these thousands, of switches across, a genome, and that fits with a lot of known biology. So. In summary I've told you about sort, of progress in the, entering. The epigenome, this. Is an exciting time where we really have, a personal. GPS for. Navigating, the gene regulation, landscape, the, concept, is that we have technologies, not to go quickly from, individual.
Patients, Or they're even rare, clinical, specimens, do, technology to find the, DNA switches. That control when. And where these genes turn on and off and that might put, us in position to develop custom, therapeutic, strategies. You.