Biochemistry Focus webinar: Human Cell Atlas - Mapping the human body one cell at a time

Biochemistry Focus webinar: Human Cell Atlas - Mapping the human body one cell at a time

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Okay. Good afternoon and, thank. You all for attending, this, biochemistry. InFocus webinar. Um my. Name is Colin Bengal I'm professor, of respiratory cell and molecular biology, at the University, of Sheffield and I'm also chairman. Of the biochemical. Society, awards committee and it's really in that capacity that I'm here, today introducing our speaker. Sarat. Eichmann, who is going to be talking about the. Human cell atlas mapping, the human body one cell at a time so. Sarah is the recipient, of the. 2020. GSK. Award. From the biochemical, society, and this is given in recognition of research. Leading. To new advances, in Medical Sciences and, I'm sure as we go through the seminar today you will get, a good indication, of the way in which Sarah's work has led to significant. Advances, and some of the work that she will be talking about today is highly, topical and, I. Should also mention that Sarah's a previous, previous, recipient, of a biochemical Society. Awards, she won the 2011. Coll with medal and. I was lucky enough to be in the audience when she gave her a presentation. At the Royal Society which. Was part of the Viking. Society's, centenary, celebration, so a very impressive talk, we had there and I'm sorry gonna get another one today so so, there is head of selling, genetics, at the welcome sinus. Center and she. Is, working, on a complex, deciphering. Of immune systems, using. Genomics, and bioinformatics approaches. And she's. Also the co-lead. Of the human cell Atlas project. Which I'm sure many of you are familiar with, and. This was launched in 2016. And, the. Approach, that the, human, cell Atlas is taking, is to, map. Transcriptional. Expression. Signals, across each individual. Cell in the human body, and. What you will see today is how this has been applied in, the current cope at 19 pandemic. So. She's going to be presenting. Her. Work over the next 40. Minutes or so and, what. I would say is that if you have questions and we certainly want to encourage you to ask questions then. Can you do that using the question, box on the.

Go To webinar control panel so, that you will be able to, ask. Those questions those questions, will be fed to me, online. During. The the process, of the talk but at the end then we will ask those questions, we will hopefully have around 15, minutes for, Sarah, to answer those questions she, has an incredibly, busy diary and I, think we probably will finish pretty much on three o'clock so. With that I think we're ready to go and I would like to hand you over to Sarah to give her presentation. Thank, you thank, you very much Colin for that kind introduction and, thank you to Colin to. The biochemistry, society, and to GSK, for this wonderful, award it's, a tremendous, honor for me to present here, and. What. I'm going to talk about today is the human body one, cell at a time, that's. What my research is about and. Of. Course each of you has a body that consists, of a fantastic, diversity, of cells that come together in different ways in, different, organs, and. In. The different tissues in our body you, can see the Airways the thymus the heart kind of represented, here the, body has many different types of cells with different shapes and functions. Every, cell contains the, same DNA pretty. Much our genome, remains. Identical. So, why is each cell different. There's. An invisible machinery. That. Determines, which genes are active in each cell and. Those. Different, sets of active genes resolved, in the specific, shapes and features. Of the cells and that's. Why we have, fibroblasts. On the. Left hand side but also neurons. As. Shown on the right hand side. Historically. We've. Used microscopy, mainly. To look at cells and that. Told, us that they have different shapes. Now. We, can understand why they are different, so. By around 2013. The resolution. Revolution, in genomics, was underway, with. Cutting-edge technology making. It possible, to sequence the tiny quantities, of nucleic acid, in individual. Cells. Anna. Single-cell, RNA sequencing. Enables, us to quantify exactly. Which genes are switched on in. Each individual, cell, so. The human cell Atlas consortium. Was. Really motivated, and catalyzed, by that resolution revolution. In genomics, and, what. We set out to map. It are, the types of properties of all human, cells as a. Basis, for both understanding. So the basic, science, mission, but. Also diagnosing. Monitoring, and treating, health and disease and that's where that the medical, impact comes in the Colin mentioned that's, related, to this GSK, award and that. I want to emphasize in this particular, presentation. When. I had the opportunity to take on the leadership of the cellular genetics program at the Wellcome Sanger Institute in, 2016. My. Ambition, was to leverage single-cell. Genomics to map the human body and my. Partner, in that endeavor became, a de frigate from the Broad Institute in, Boston who. Shared this vision and she, coined the term human cell of us for that aim. Over. The past four years the, human stylists, has taken. On a global, membership, that's constantly, growing and. I want to emphasize that if this is an open bottom-up. Grassroots, scientist. LED community. That. You can join we have almost 2000 members as you can see from countries, all over the globe over. A thousand different institutions, and. You. Can every. Scientist, can join as an individual, member or register, their research project, at human, Southwest org slash join HCA. What's. Important, to our community, is not. Just the. What we do but also the how we do things and. To. That end, too, we, formed. An equity and diversity working. Group as part, of the project and, it's. Led by Alex Trebek furthermore journalism, manga, and. For. The the how we do, things, we've. Formed biological. Networks. As. Well as, the early working groups that are focused, on analysis. Methods the computational. Analysis methods that are at the heart of the project and. Standards. And technologies, working group and metadata working group and these. Biological. Networks are. Communities. Of scientists. And focus on the individual, tissues and organs, that. Our body is composed, of and so we have about 12 now, that. Cover, many of the major systems. And nature tissues. So they're about 50 tissues on the body organized into 14 organ systems and we. Also have working groups for human development and Breanna canned fetal development. Orgonites. And genetic, diversity and, you. Can you can contact, the coordinators, of the each of these working groups at human cells, org slash coordinates. So. The technologies. As. I, mentioned at the beginning are really. What catalyzed, and. Galvanized. The community, I think.

Around. This project. At. The beginning and it. Was really this evolution. From convention. About genomics. Which, is the fate here, to. Single-cell. Genomics. Early. On and now. More recently, spatial. Transcriptomics, which. Is putting the pieces of the cake back together in two, and three dimensions and tissues and it's. The combination of these methods. Integrated. Through computational. Statistical. Methods, that. That is going to build our Shin sellouts. Now. I just, I want to show you one little movie here I think I've got a couple of movies. My. Presentation. If, this, works. Which. Shows in using microfluidic. Chip technology. Which was one of the the, early, scaling. Of. Cells how cells are captured in individual, chambers, in. Microfluidic. Chip so this is one of the ways that cells can be captured other. Ways, are the more common now or the michaelis lytic droplets, nano. Well plates, and. And. Multiplex. Bar coding methods, in, split, seek approaches. But. You can see here that the the, principle, is that cells were isolated, and and. Bar. Coded individually, so. That the the nucleic acid that's released inside each individual, cell can, be tracked to the origin of that cell and and. In. This. Movie from. From flute on you can see how the the. RNA is released through, cell lysis, then. It goes on top of a cave from a library prep and sequencing, and of, course computational. Analysis. Downstream. And. And. It's through the. Single-cell. Genomics as, well as the spatial methods that we want to build this Google map eventually and get. To the definition, of individual, cells inside, the tissues, in their, micro. Environments in their niches and. Define. The, the. Individual. Cell circuits, the individual. Cell. Neighborhoods. That. That are occurring. In our tissues, and. So that's really the, the. Vision the, principle, of the human cell Alice now. This. Is that the GSK, talk of the biochemical, society. Which. Is for advances, in medical sciences and so a, logical. Question is what is the impact, of having.

Such A reference, map, what's. The meaning basically for medicine and. There. Are lots of different, implications. That the having a reference map has one. Is that it can provide. That. That framework, for, understanding what goes awry in disease what changes, in disease and in order to understand, those changes, you need to have a healthy reference, in the first place. It. Can provide a framework for drug discovery, and I'll, show you, maybe. Some examples, later that show how how mapping, the endometrium. That the uterus, basically, opens up options. And. Fields. Of drug discovery and unexpected. Areas. Understanding. Drug toxicities. By. Knowing, where. Come. In a comprehensive. Way a particular, teen is expressed all over the body and all the different tissues. Rare, disease, variants. Diagnostics. And. Regenerative. Biology and. In. This talk I'll focus in particular on, the. Regenerative biology the. Organoid, models, tillu. Engineering, in other words and on, disease mechanisms. Where do we stand in terms of how far we've gotten in. Terms of, opposing, data and single cell data points, in this project, if, you go to the. Data coordinator platform. At human, data documents, all at West org that. You'll find about five million cells, in, total, and here are examples of different tissues the kidney, the. Skin the. Lung the, gut where there are almost one-half, million cells, from. From over a hundred different human, individuals, and many hundreds of samples. Embryonic. And fetal development of, course that encompasses many different tissues and. And. That in itself. Encompasses. Data. With. More than four million cells of. Course spread across different developmental, time points, the. Liver. Is. Starting, to in mark even though it's a challenging, tissue, to handle and. And. So this this gives you an idea of how we're building up the human body step, by step through, our, biological, networks by, focusing, on these different. Organs. And their. Characters, and. So. What I'd like to go on through in this first, vignette. Of three that I'll tell you about today is. Our. Unpublished. Ongoing, work on the cell atlas of the human uterus, and this. Is work led. By Rosa Vento who. Used to be a postdoc in my group and now as a junior. Group leader at the Sanger. Institute together. With another rising. Star margarita, Turco from the pathology, department at the, University of Cambridge together, with my own and. And. In. A broader collaboration, including. Also over power actor a national Moffitt's group, now. The the, human uterus, has, is. A challenging, tissue to study because of the dynamics. Of the, changes, in the tissue throughout, the monthly cycle so you've got the shedding current. During the initial phase of menses, then, there's a proliferative, phase where, the cells are cycling, and dividing. To rebuild, the mucosal, lining.

Uterus The endometrium and, then, a secretory, phase where. The cells produce secretions, epithelial. Cells produce secretions that provide. Nutrients, for the embryo and that's the window implantation, at the beginning of pregnancy and. A. Lot. Of the features of this tissue are specific, to primates, so. Including, the spiral, arteries that. Are. Part, of the the structure of the endometrium and. So. Together with the dynamic changes, and the primate specific, features of the tissue it's. Been it. Hasn't been tremendously. Accessible. For. Molecular. And cellular studies, and. So. Therefore. Using, a cell Allis approach so a combination of, single cell and single nuclear RNA sequencing, together with using, spatial transcriptomic. Mapping, which you can see. In. The, three part the two panels at the bottom and on the right hand side of this slide has. Really opened up a window of new, knowledge, about the cells and their, locations, in the endometrium, now, starting, at. The top left here, if you can see the red laser pointer I just, want to explain very quickly that the myometrium is, the the smooth muscle, layer that surrounds the uterus and that's where you can see at the bottom and, then the endometrium, itself consists, of three different layers the, basal, layer which, is the. Location that it's thought where where the stem cells reside. Although. That's best I would say still an open question. The. Glandular layer. In the, middle and then the luminal. Layer. At the top towards. The inside of the uterus. And. So. What. We what we need to have. Is tissue samples, from these different stages and. Traversing. The, full depth of these different layers and. You, can imagine that getting, human, tissue samples. It. Can be challenging because people don't voluntarily, so give up their tissues and so. The the. Sources, of these samples, have been biopsies. From. People. Who are be biopsies for diagnostic, purposes and then end up being they, end up being healthy biopsies, and also. Tissue from deceased transplant, donor. Tissue. From, the Cambridge ba repository, for translational, medicine, transplant. Surgery course I have Parsi and colleagues whom we've worked with closely and. And. These biopsies, are very precious because they transmit, they traverse the full depth of the tissue where's the biopsies, or the - one. Or two top millimeters, of the tissue on the, inside of the uterus and. But. The biopsies, can cover. More. Different stages in a more dense way than the deceased transplant, donor tissue which is rare. And precious for. All the tissue is precious but the transplant, donors are particularly. The female current, disease transplant, donors are particularly rare.

So. What. We did here was to take these samples and as I've mentioned subject. Them to to single cells single new cornea sequencing, as well as spatial transcriptomic, analysis, and what that is are tissue. Sections, on. On, trips which, are 10, external max lithium chips and you can see here each. Sequence. Feature of the triple is is is shown as a circle, on. Top of the, the. Tissue, section, and it's about 50, microns in diameter so, it consists, of more than one cell. And. It consists, of about 220. Or even forty cells depending on the exact, architecture, of the tissue and we. Can de convolute, we can deconvolve. And. Predict, the cells in, each individual, feature, by. Integrating. With the, single. Cell data and we, use a, framework. That's unpublished, on tits on github by, vitaliy clipped classic nickel from Homer Bart just will cut cell to location, and what. That allows us to do is to. Locate. Where, individual. Cell. Populations. Are sitting, throughout, the. Depth. Of the tissue sections, and so what. This is showing is, is on the top right markers. For, glandular, epithelial. Cells so, what we can see is that there they're. Sitting in these little the Colangelo, epithelial, cells are sitting in little clusters around the glans. Which. We can identify in, terms if they are full transcriptomic, signature, and multiple, subtypes, of these epithelial, cells that i won't go into here in detail, we. Can also identify, different. Stromal, cell populations. These are these are essentially, structural, cells that form, kindness. Some of the, strength, and structure of our tissues and, we, can identify different. Subtypes, of these fibroblasts, these stromal, cells and what, you're seeing here is a so called distinct deciduous, stromal, cell population, that, we also observe, in the decidua, which is the the pregnant, version of the endometrium, and. Where we published a cell alice on, this, the decidua, a couple. Of years ago in in nature arose or rental again was the first author and, we can see that that that, butter bus population. In the, endometrium, in the luteal phase sits. In the top layer, here. Whereas, a different, fiberglass population, that's, specific, to the basal layer you can see sitting in the bottom left and then, why is it important, to understand the nature of these different stromal cell populations, it's, because they're secreting, different signals, and those, signals are. Determined. The. The cell fate of the epithelial, cells and so there's a I'll, talk a little bit more about this in the next slide on, the, bottom right we can see that there are ciliated, epithelium so, that align the lumen that's it on top and we can the. The dark red color. Pops. Up on the luminal, layer because, what we're highlighting there is markers, of that particular, cell population, which has the cilia the hairs that move, secretions. Along the uterus and and. What. We can also see is that there's an LTR v positive, cell populations, it's also sitting in that, luminal. Layer of the endometrium, the in the luteal phase in. The secretary phase. Which. We think is an, intermediate progenitor. Population so. The stem cells are unlikely to be sitting in that top layer but we think these LGR v cells are intermediate.

Proliferating. Progenitor. Cells. Now. I, showed, you that spacial, transcriptomics, data which is which may seem coarse-grain. But it's incredibly valuable and exciting, in the, sense that it's. Comprehensive. In in. Terms of the genes that it covers, and so it really it's, it's really an unbiased, mapping. Of. The. Expression. Pattern across the tissue in that two-dimensional, tissue section, we can look up every single marker, like that LGR v that i showed you or. The markers of ciliated cells and so on in, the data and find out where the cells are sitting but. To really map. Them at single-cell, resolution. We need to go back to put on microscopy and, this. Is a very fancy multiplex. Fish approach. Here from from Oviraptors, lab, who's. A brilliant, technology. Development, P I in my program, and they've. Developed a quantitative, multiplex. Fish high throughput platform for. Mapping really, large tissue sections, so kind of taking us to that Google Maps view, of tissue sections, and what's. What's mapped at the same time here stopping, for the nuclei and comforta, theö cells a matrix. Metalloprotease, marker, and pap. Which the glandular epithelium. Expressed. The sequences, Secretariat, sanyo cells and. In. Each of the of the columns, you can see different phases of, the menstrual cycles, a proliferative, mid. And late and then the secretary phase mid and late and. What. These beautiful images show, us is the the, development, of the glands and. The. So, in the in the early proliferative. Phase there, are very rare, glands, that are quite isolated. Expressing, mp7, PAP, with E with, the outcome marker in terms of those two cuando epithelial, cells and then you see a massive rise in expression. In mid and late proliferative, phase and by, the secretary, phase those glands are. Sort. Of a mature and well formed, and, the. Pap expressions, is is really high in the upper glands, and, an. FM p7 cross back to the deeper glands. And. The. Pap is dominating. In the mid secretory phase and then the drawer of the epithelia, and the mp7. Matrix, metalloproteases, restricting. Back. And. And. Finally we have we have mp7. Expression, in the lumen I'm, getting. Ready for menstruation. And so, you can see, from this imaging and we, can quantify each gland, in terms of the expression levels, of these, or other markers, in a very quantitative way you. Can see how this gives us, exquisite. Resolution, and. If, we if we integrate, this data with. The spatial transcriptomics, data then. We. We get the best of both worlds with the benefit, of the high resolution, as well, as the unbiased mapping, of all transcripts. So. That's, it's it's what I want to highlight here is really how the combination. Of single-cell, genomics with, spatial transcriptomics with. Multiplex. Imaging, can. Get us towards the Google Maps of the human body in this case of the uterus, throughout the menstrual cycle now. What I said I want to emphasize in this talk is the the utility, of the, human cell atlas or. In. In the context, of different applications. And to nearing of tissues and disease and. For. The in for the for this example of the uterus. The. The. Mapping itself I want to emphasize has, a lot of implications for disease because we can integrate with G was. Associations. For endometriosis, and. Endometrial. Cancer. And so on so forth and start to identify new drug targets but. I don't want to go into that in detail here instead I want to show you how. We. Can use endometrial. Organize which were developed. By Margaret, Tudor Cohen and published, in Nature last year, to. Understand. The cell fate decisions, of. The epithelial, cells in more detail so, endometrial, organoids, are, in, in this context, there are adult stem cell derived so, basically they're derived from a tissue sample that's digested, and then, grown. In mate return and. Over, a couple of weeks and. That. The the idea clear is that we want to. Connect. The invivo cell atlas of the uterus with. The single-cell transcriptomics, data of the in vitro models, in this. Case they're they're stem cell types organoids, but. The same principle, could hold for IPS ripe cells orphan whites and then. Sora. See whether we can use the the invivo human, cell Atlas data to. Improve in, vitro systems, but also the. Engineering of the in vitro system, helps us understand. Mechanisms. And writing pathways, in the that. Are occurring, in our adult tissues or, in our in our homeostatic, healthy, tissues. So. You can think even, in a way you think, of the the human service as providing, the ingredients. Or the recipe it's, like a recipe, cookbook. For. Making, tissues, in vitro so. The human cell Alice tells us about cell, surface receptors, secreted. Factors and, and and. And. Ligands, for the cell surface receptors, it tells. Us about the cell culture medium therefore, so what are the secreted, factors that these cells are living in in vivo. What's. The kind of. Matrix. Depending. On the tissue not, not relevant for the uterus but for other tissues, their scaffolds.

That The cells are growing growing on and so. In a way you can, think of the the. The, in, vivo cell. Atlas as as. Providing. An initial. Set of instructions, for how, to grow, tissues, in, the dish and and, send different signals to, the epithelial, cells and mature. Them down different lineages, and then, what. We did was for that we can recapitulate, that in the diff and make it make on the one hand organoids, that, are dominated, by ciliated, cells and they have a kind of twisted, morphological. Appearance, and we. Can show, by a single cell RNA sequencing, that they're going down one linear trend by using knotch signaling we could push the organoids. Down. The secretory lineage, to show that they. Recapitulate, Colangelo, epithelial. Cells with that more columnar. Morphological. Structure so. We can we can engineer, the culture, we can engineer, the cells in vitro based. On the, recipe, book that the human cell atlas data gives us. And. So. That's the one. Of the the medical or. Translational. Applications of. The human services is towards. Regenerative. Biology and, in. Vitro systems. Now. In. In terms of. Medical. Applications, of the the, uterine system, I mentioned that a couple of years ago we studied the decidua, which is that the decidua, lized version, of the, endometrium so to speak which is, the. The. Next phase of the endometrium, after the embryo implants, and. When. We when we studied. That those, tissues, in detail, what. We could see is is what, we could find is, is different, natural. Killer cells subsets, on the maternal side, that. Are. Talking. To that are signaling, to or in in a signaling relationship, with. Fetal extra, billa's trophoblasts. So these are the cells outside the, embryo that come from the fetus and have the paternal, alleles. So, basically, the maternal, immune system, these, NK. Cell populations, are seeing paternal. Allele z' and of, course this is a kind of conundrum for the immune system because in. A normal context. The. The T cells and NK cells, and so on would recognize. Non-self. Allele so. Basically. That's the principle, of the. Challenge of transplantation. It's. What should happen for clearing, tumors, basically, in somatic mutations, but. Here there's a context, where the immune system needs to, tolerate. Those paternal, antigens, and what. We were able to, identify. Is. The the immunosuppressive. And adaptive. Innate. And. Adaptive. Cell. Signaling. Complexes. That contribute, to that in. Toller amino modulation, and the the, peaceful, kind, of inflammatory. Yet. Peaceful environment that, occurs at. The maternal-fetal, interface, and so, this has implications, for. Reproductive. Medicine and reproductive biology, as. Well. As other contexts tumor tolerance, and. And. So on, by. By, opening, up the cellular, and molecular mechanisms. That, are occurring, in this human context. Which, is quite different from the mouse would, say. Another. Disease context, I want to. Touch. On briefly, and. And, that that, funnily, enough also relates, to a collaboration. With GSK, is. The. Seller atlas work. That we did on. Bronchi. The. These these large. Generations. Of the the Airways in the lung and and. Also, I realized that the prank among laughing which. Is down. Inside. Whether small, generations.

Of. The. Of the airways and the lung and we. Compared, the healthy reference data to, bronchoscopy data, from from asthmatic, donors. From asthmatic, patients, and. Of. Course in asthma, basically, the the there's. Information, that leads to thickening. Of the of the muscles, and ultimately an asthmatic, attack, and, the. Approach that we took here, was. To to. Use. Bronchoscopy. Is, from that were carried out in collaboration, with, with, our partners, in croning in in the Netherlands. Martin. Now in and Martin, wonder burger and and. These are bronchoscopies. Samples from living donors and then parenchymal, from, deceased transplant, donors from the deceased transplant donor program, and used. Now. I want to focus on suspension, cell data. From. From microfluidic. Droplet single-cell, RNA sequencing, so this is different technology, from what you saw before which. Allowed us to unravel, the, different decipher. The different cell populations in. The, two different if news of the lung so the. And. And the tissue architectures. In these locations are really quite different as all the cell populations, so, you've got the type 1 type 2 alveolar. Cells and the parenchymal where's this, goblet. And ciliated cell populations, are epithelial. Cells, in d and, the larger. Generations. Of the lung, and, as. Part of this work we also profiled. The. Upper Airways in other words the nose from, from a small number only two donors and. I. Want. To emphasize this because I'm going to come back to this, and. And, what we found was two different goblet, cells populations, in a different, to the ciliated, cell populations. And those, two different goblin cell populations. Included. One population, that you can see highlighted here. That. That, is has, a more, kind of, activated. Innate immune. Signature. That. Implies. That it's likely to interact with dendritic, cells t-cells and so on and it. Later we later realize, that this goblet cell population, also has a much higher is, to expression level which then became relevant, in the context, of Tsarskoe b2 and the. Czar's co2, receptor, and. Just. Sort of backtracking, to the asthma, study for a minute, what. What I want to mention. Very quickly again was the in this collaborative, work with our colleagues in the Netherlands, and in Germany, and at, GSK, we, found that the. The. Asthmatic. Donors, had. A. Sort. Of aberrant, pattern. About the epithelial, cells in the bronchi and they, also had very specific T, cell subpopulations. And those specific. T cell populations. Pose. Basically. Potential, targets, or from novel drugs in asthma that aren't that aren't particularly. In patients that aren't steroid. Responsive. So. I'm going to use that to stake over into. The. Final story of this talk which is going, to be on covert, 19 as a scholar. Mentioned the very beginning and so. As, I mentioned, in this in this work where we were primarily interrogating. A somatic versus healthy donors the. The data from the nasal cavity showed this goblet cell population, with highest to expression, and. And. We, discussed it very generally, in the paper or mostly most in supplementary, material. But. Then of. Course. Earlier, this year when. The, pandemic. Kind. Of hit, us, very. Very rapidly. We. Went back to our. Airway. Data and asked. Where. Is, is the SARS Coby to receptor, expressed. In which cells that Express and can we generate. Hypotheses. About where the corona virus, might, be talking in terms. Of the tissues in our body in, the, in those very early stages, of infection and, when. The virus is entering and that's, relevant for the airways it's also relevant for other barrier tissues and this. Became a. Brilliant. Collaborative, project, across. The very. Broad swathe, of the, human sailors community they even even includes :, by. Transfer, who introduced. Me and. That's. One of the great things about the human stylist, it's, it's it's a large community but it's a really small world and. What. We were able to do then gathering.

All The published data from this human cells but also unpublished. Data that people incredibly. Generously, shared, including. The cornea, and, conjunctival. To, eye tissue. But. Also liver and, heart, and other tissues, people were very you, know in this exceptional. Situation, where speed is of the essence and science is part of the solution out, of this pandemic the. The collaborative, spirit has just been. Incredibly. Encouraging, but, it's. It's catalyzed. And. What. We ask basically, and all these tissues, is whereas. The, receptor, expressed. And. And mortem, sonar and neat, one in my group went, about. Interrogating. Different, tissue, datasets. In a systematic, manner and. I'm. Going to start with the nose again where I told you about that, goblin population. That has that is that's really high in the age to but also the. Ciliated, cells kind, of as previously, expected express, the receptor. And. Of course the the nose is really relevant in terms of those early, phases of. Viral. Entry and. The. Understanding. Mechanistically, the high transmissivity, of this virus as compared to other viruses, in, the. Lungs we've got club in the, lower areas club and ciliated, cells that. Express the receptor, and. Then. Down in, the alveoli, in the lower parental. Tissue of the lung the, alveolar, type 2 cell which was the first cell that was described, using. Antibodies immunohistochemistry. As, expressing. The. Receptor, in 2004. As I mentioned, Linda Lakers group. Shared. The, ACE, to expression profile, of her own published eye data and what, this revealed was that their superficial country, tribal cells in the country tribes or the whites of your eye that. Expressed. Have had high levels, of a stew and tempers. To the protease that goes along with with, stripping the surface. Of the virus and allowing viral entry and. Then in the in the cornea. There. Are also corneal. Epithelial, cells that have good levels of expression of a stew and that. Understanding. That is, is, in of, course the the eye connects, the nose and, it implicates, the, nasolacrimal. Doctors, are a mechanism, of infection. And transmission. Other. Barrier, tissue is where the virus could be passing through is that the gastrointestinal, tract, basically, started in the mountain and going down to the rectum and the ileum the small intestine, their, entry sites. That. Have high levels of ways to expression and. I. Also, want to want to mention there are cells, in the placenta have both perivascular, cells, and also fibroblast, populations, that do Express as2 and this. May be potentially. Relevant understanding. Vertical. Transmission from. Maternal. To. Fetal. Transmission. Of. Of, the virus it's, very rare that, that happens but. This. Could it could help understand, the mechanisms, that may be involved in. Maternal. Fetal transmission and, so. With. That basically, I've taken you through. A tour, of. Different. Diseases where, human. Solace data has provided insights, into mechanisms, so I've mentioned. The. The. Asthma, example, and covert 19 but, but I also went to the NK cells which are known the. NK cell extra billa's trophoblast interaction. Which is known to be. Involved. In incompatibility, between maternal, and paternal alleles. Accounting, for 13%, of, from preeclampsia. And miscarriages. And. And. Understanding. The details of, which cells are involved one of their expression patterns, is, important. In understanding. And. Then ultimately also, addressing. And treating. Diseases and. So. I. Focused. On got the Airways to, some extent and also D, the. Uterus and that, brings me to the end of my talk thank you for listening and I'll be happy to take questions. Thank. You very much notes, it's particularly, difficult to, be sat in your in your front room or whatever talking, to an audience it's, not a particularly, easy thing to do I know, we're.

All Having to get used to it so a, very. Detailed. Presentation, about, the value of the human cell outlet so it's um obviously, some specific, applications, so so, we do have a number of questions. As, we thought we probably would and I'll kind of go through them as I can find them so I'm going to have to peer down at my laptop to be able to read them sorry and so. So really the UM the. First question, and. That. Has come from some Kathryn, McKinney's which, basically, asks, is, it possible using, the single-cell, RNA seek approach to. Resolve. Different. Transcript. Isoforms. For genes obviously, many genes are alternatively, spliced so, do. You have the read depth to do that. Sorry. For full-length protocols, yes. There. Are a couple of different, full-length protocols, so. The. Smart C was the original one from Ricard Sandberg and then smart seek - which is a variant, thereof. We. We've published and. Related. Protocol, that uses a different set of enzymes NEP. Enzymes, that we've put on protocols, to i/o, and. Submitted, for publication, the. Challenge, for those protocols, is throughput. For. Those full length protocols, and. That's. Why we've, we've, published an. Automation. Workflow. If, it's, by, Li Ramon over on my group on its upon protocol style. Okay. Thank you so Mike. Robotics. That. Are that are inexpensive. Right. Okay, so thank you so the the second question, really from. Dylan both sort, of relates our guests to the to the first, question as well which is what. What are the limitations. With sort of sequence read that in single cell sequencing and, and. Are. You comfortable that you can get a complete. Transcript, term of each individual, cell or a representation. Of each individual. Cell. So. The. There's. No there isn't one. Single answer to that so it depends a lot on the protocol, so we talked about, smart. See any, be full-length, RNA sequencing. Just. Now in, the previous question, and and, then my talk I've presented a lot of data based, on ten extra Norman's droplet microfluidics data, and the each protocol. In, a reader, dependent, manner. Varies. In sensitivity, and so, I, would say the you know one of the most sensitive ones are smart seek that full-length protocol, with. Saturating. Read depth and you can get down into the single digits of. Detection. Sensitivity, using. That approach so. You can detect on the order of one to ten molecules, of mRNA per cell however, through. Point is limiting, and, so the, the droplet microfluidics protocols. Are more widely adopted and used because. Of that and and, they're they are less sensitive but, the, benefit. Is the. The. Larger numbers of cells so. Yeah. It really depends on the protocol, and and one, way of addressing the sensitivity. Question is is for instance by by. Integration, or deconvolution, of, alternate, with single, so. But. But yeah it can. Be an issue depending, on your research question, for instance centrally who focus. On very very lowly, expressed transcription, factors or something like that and. I guess, it's sort of the, the, consequence, of that is that's, particularly, difficult in very rare pub cell population, so obviously you're aware of the way that this. Thing will sell out single, cell workers to, kind of redefine the airway epithelial cell. Population, so I guess if you've got a particularly, rare so rare cell. Type within a within a tissue then it's going to be difficult, to completely. Gauge. The transcriptome, of that cell. Well. Unless you enrich and then dig drilling you, know with a full-length highly. Sensitive matter for instance so. You'd have to do it an iterative way you have to discover, with, a, large. Breadth method, and then trillion, you, know in a second stage. Okay. Thank you. Next. Question we have from Anne, Harris, is. Very. Complimentary, about your endometrial, work which I think you've told us is just about has.

Just Been submitted, so and, so. Are. There, cells. That are shared. In common with other epithelial. Cells you refer, to the ciliated, cells, and does. This the human cell at this project have a kind of comparative, bioinformatic, analysis. That will allow, you to, study. The interaction between these, different cell types and tissues, from a sort of an evolutionary, perspective. Okay. There's. So there's lots of different questions. Actually. In. That question one is. Comparing. Epithelial. Cells across different tissues of the human body and. You. Know we've done some in our PD Keun and batch of aggression paper published in bioinformatics, not. Too long ago and, we. Did a sort. Of overarching. View, of Mouse cells. And, the, epithelial, cells are actually amongst. The most different, of. The different cell in is so so immune cells vasculature. Fiberglass. And so on the. Populations. Are slightly different in the different tissues they adapt they have kind, of different variations. According, to the tissue but overall. They're pretty similar epithelial. Cells which often, form. Kind. Of functional. Part. Of the tissue if you will they, they tend to be really, really specific to that tissue so yes I you're you're right that I mentioned, secretary. And ciliated cells in the endometrium, and and, goblet and ciliated, cells in in the airways. But, that's slightly misleading because they really have very very different personalities. It's just those growth. Features. Those, gross categories, that that that, occur in both tissues, and then, the second part okay I think I think I see where the question is going so the second part was on the. The. Evolutionary, relationships. Between. Cells. And. You. Know I think that's, or. Between tissues I think that's a super super. Interesting question. You, know because the the different, tissues are so different to each other one. Way of addressing that, may be took to make cell atlases of you. Know the whole. Tree, of Life essentially. One. Day yeah yeah you laugh I mean it's, a funny idea now, it's, a crazy idea but you know the human genome project was, completed. Around 20, years ago the. Announcement, was exactly 20 years ago this past weekend, and we. Now, have the. Earth biotene, own project, and the Darwin tree of life which. Is sequencing, all living. Living. You. Know organisms, on the planet and, so, you could imagine that someday you would have a surplus of, the Tree of Life of every living, organism on the planet and dad would allow you to, to. To. Reconstruct. The evolution, of cell types in a very accurate way in the meantime. You. Know they have to be proxies. Okay. So. In wishing, to generate, more, work for everybody involved in this project, so. Is there going to be an interpretation, of, proteomics. With with. The transcriptome, it's because of course you can have a gene. And not necessarily a protein you can't necessarily have a protein without a gene so so. Are. You, graded. Okay. Not in time, yeah. So. There. Are. Proteomics. Initiatives. Related, to the human cell Atlas so. One example is Neil Kelleher, was part of the hub map project. Which is the NIH funded, counterpart, or. Truths. Allied. Project, to the human cell atlas and there. There's, like. Proteomics sub projects, and eventually, the idea is, to systematically, purify. And we've talked about purifying, where cell populations. Systematically. Purify, um. Cell. Populations, from from, tissue cell populations that are discovered by the human stylist and then, get. Enough of them to do proteomics because of course the challenge was proteomics at the moment the reason that it's not the. Method, of choice for the human cell Atlas is that. The. The. Unbiased proteomics, requires. A, good amount of cells input material, it's, not single, salads you're talking thousands, really, so. How, much yeah. Obviously, it's it's, it's pretty difficult with the sensitivity. So so. I've. Got a couple of shorter questions, which may have longer answers of course but and so, let me been able to detect mitochondrial. Transcripts, in the single cell systems. Yeah. So. The.

Answer Is yes, to both mitochondrial. Transcripts. So. They're mostly mostly, Matakana mitochondrial. Transcripts, of nuclear, origin, I don't. Know whether they mean mitochondrial, transcript to the nuclear origin or mitochondria, transcripts of mitochondrial, origin. But. I mean there's some detection, of both. And. The. Mitochondrial. Transcripts nuclear or internal you know the. The, the. Primers. Of poly poly. Poly, a tail. Primers. So. The transcripts. That the. Type of poly a track will be. Will. Be detected preferentially. And but. There's always a. Good, fraction of mitochondrial, transcripts, that are detected, and. As. You may be aware an, excess. Of mitochondrial. Transcripts, is often used as a proxy of a, low quality or apoptotic cell, of. Cell deborah's of a cell that has a, permeable. Eyes plasma membrane, and that's leaking, cytosolic. Transcripts. And is enriched in in in transcripts, that are mitochondrial. Inside. The mitochondria and, more protected. So. The answer is yes to both types of transcript okay yeah. In short crisp in longer answer and then another. Short question so it is a stew, over expressed, in a somatic tissue. I'm. Sure. The answers in the papers. It's. A great question but that you know the funny thing was we weren't focusing, on that in that paper of course because that, that's what came later that. Analysis, I mean, what a. Stew. Does come up as I meant from kind of with her with, that immune activation signature. That I focused, on in the nose but it, does also happen in the bronchi, and. That. There is that co-expressed, module, of genes and in fact it also in the gut and. And. That. Module is more common. You. Know loosely, put under stressed conditions. So. My gut feeling is that the answer will be yes but, I have to go back and double track, ok. Another. Short, question, which has probably got an incredibly, long answer, is if you had any unexpected. Results. I. Would. Say you know their surprises. Every, experiment, opens up so, you know new surprises, and every. Tissue has, sort of unexpected new, cell types and, I. Think that's one of the, you. Know incredibly, exciting. And gratifying things, of working on this on, this project and it's it's I, think if the whole community, really enjoys that those. Unexpected new, discoveries, and it's the power, of this. High-resolution, technologies, and. You. Know I want to I want to emphasize, that it's it's. Thanks, to their to the. You. Know incredible. Revolution. In these transcriptomic, technologies. In. Terms of high resolution spatial. Resolution, and so on that's, enabling, us to make these discoveries. Yes. Good. Example, really is is the work that came out a couple of years ago I find.

The Ayanna sites in the airway, yeah. Yeah. So that's that's a very, that's. That's that's at issue that's been did you know, studied. Took decades, you, know for Debbie the. Endometrium. You know to me the decidua, that, that paper you know. That. Really ripped open the, zone aided architecture. Of the the decidua, the pregnant. Form of the endometrium, I mean, that was full of surprises, and has been cited hundreds, of times in a very short period of time because. There it. Was known that there that there are different layers in the, endometrium. Like in our skin and I mentioned those different layers but the cells that, compose, the different layers, were. Pretty much a mystery, and it's, that the single-cell, transcriptomics, and you. Know the fact that we were able to access the tissues and then studied. With these technologies, that completely, you. Know switched on it was like switching on a light bulb I think in a way in terms, of explaining, that. The detailed cell populations. And molecular. Expression. Patterns of them and so on okay. Thank you so so I think in. The interest of time we just have one last question if that's okay and and. It goes back to the the first part of your tool which is the spatial transcript. And, the. Question says how do you resolve small cells, that are close together. Relative. To so. You have RNA from multiple, different cells yeah, yeah. So, I went, over that very quickly because I was just trying to cover a lot of things so, the only way to do, it is, computationally. To. Get to a pseudo, single-cell, resolution. From. The data in that spot, for deconvolution, and. What was. This Bayesian, model called cell to location, that's on github by a battalion ethnic, of in my, program, from our artists, group and. And. Vitaly is a brilliant. Student developed, out there are there other deconvolution, methods. That can be applied to so, it's only through, the integration, of the. Two different. Data sets that you get to the quasi, single-cell, resolution. Okay. Good, okay well thank you very much so so obviously I think you alluded to it before how, rapidly, the technology is changing here so maybe, in. A short space of time some, of those difficult. Spatial, questions. Will be more easy to address perhaps and. So I'm, counting on that, I'm. Sure right, so thank you very much so a fantastic presentation, so, I'd, like to thank you on behalf of the biochemical society for the, presentation.

And. Answering. The questions, that have come in so so. I'd like to say too to. The audience in general that the biochemical, society, are running these webinars. On. Demand, on. Suggestion, so if anybody has suggestion. For future, speakers. Then please contact the the events. Office and they will be able to consider. Those obviously. Some of our lectures. Are very specifically, at the moment, filled. By our awardees, and I think then our next one will be that in next, week I believe but in, general, we're we're open to, suggestions. And offers so again. Thank you for attending, thank. You Sarah for your presentation. And, thank. You very much thank. You. You.

2020-07-21 14:24

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