CellPress Webinar: Spatial and Temporal Genomics in Cancer Research
OK, hello, everyone, very happy to be here and thank you for joining us for our Cell press webinar on spatial and temporal genomics in cancer research to tie Miao-chih Tsai, senior Editor, cell. And I'll be your moderator for today's webinar. Technology advancement allows us to dissect the spatial and temporal changes in genomics compositions gene expression. Cellular morphologies and cellular environments during development and disease progression of various data collections led to the emergence of spatial and temporal genomics as a new Avenue of research. There's a significant interest in method development and clinical use of information for cancer diagnosis and treatment as they may help to elucidate the basis for heterogeneity of cancer. So in this Webinar, Miriam Merad from Mount Sinai, Fei Chen from Broad Institute of MIT and Harvard and Steven Wang from Yale University will discuss the directions of its further development. So before we get started with the talks, we have a few political points. So each of our speakers will talk about 12 minutes following the talk. We will have
a Q&A session that covers all three presentations. If you have a question for any of the speakers, just click on the question button, and then type it in. You can enter a question at any time. No need to wait until the end. We will try our best to parse out as many questions as we can.
It is helpful if you can include the name of the speaker to whom you would like to the direct your question. So before we begin, I would like to thank our speakers for making the time to join us today and also our sponsor OriGene for supporting the Webinar. So and now kicking it off is our first speaker, Miriam Merad. Miriam, thank you for joining us today. OK, so my great pleasure to be here. Always a pleasure. Let me just make sure I'm sharing my screen. Can you guys see my screen? Not yet, not yet. OK, we do this because even when you. It's working now. Yes all right, perfect.
OK, sorry. I think we just lost the Mriam, but we will have our second speaker to go first. Fei Chen, who is with us now so Fein? Thank you. Thank you for the introduction. And I'm very excited to participate and thank you for organizing. And really, as Mel said, the session, I think, is divided into both biology and also really the recent interest in New tools for our ability to map, basically bring genomics into the spatial context of tissues. And that's what I'll talk to
you about today. Just a quick disclosure slide. And I think, you know, I think there's no way to really talk about this without first being into the discussion of single cells, transport mix. And I think one single cell transorbital mix is one recent technological advance that has recently really revolutionized our ability to study molecular mechanisms in cells and tissues and in tumor biology. And that's because you can take a tissue and break it down into its constituent
cell types and sample the transcript of each cell. And this is comprehensive and it gets at the molecular programs that are driving the function of cells and the dysfunction of cells in disease. But I think fundamentally we have to remember that to collect this type of data, we need to dissociate the tissue. And this is where we lose the spatial and temporal context about where cells come from. And I think that context is very paramount to studying. Basically, health issues are organized, how cells communicate with each other and in particular in cancer. It's very
important for understanding the disorganization, heterogeneity of cellular structures, as well as maybe there's emergent multicellular niches and hubs which are really important for, I guess, the immune response or also tissue dysfunction. And so the other thing I think we got we have to remember is that cells and tissues are very also very dynamic. They might migrate. They differentiate a divide. And almost all of our tools that we have are at our disposal today are
kind of snapshots. Right we can freeze the tissue or we associated with it and we lose the context of those dynamics. I think those dynamics are very important to excess. But today I'll really talk about my lab work on spatial genomics, although we're very excited about the context of using molecular recorders like lineage tracing to study the context of dynamics within tissues. So so today I'll mainly focus on a technology for assessing tissue scale, spatial and tissue spatial genomics, wherein you can retrieve the transcript terms of cells across tissues. I think it's also very interesting to think about the structures of molecules within cells, such as the genome. And we're very interested in being able to do that as well. I think that will be
maybe the topic of the next talk. And so I think why why are we interested in resolving tissue organization with spatial genomics? Well, I think we're motivated by three main classes of problems. The first is, you know, as we are becoming so adept at collecting single cell transcript terms, I think the most na ve question is like, where are these molecularly defined cell types located within tissues and how does that organization relate to function? Beyond that, I think we're also interested in how are within these cell types, what is spatially differential gene expression patterns? Right and within a cell type like this, it's location in the tissue explains some of the variability in its gene expression and how what percentage or what aspects of its variability in gene expression can be explained. And then, of course, the lastly is like, what are the gene expression changes with respect to the pathology? Right we've known for a long time that we can go in and annotate tissue changes morphologically, such as a case of acne. But how do we connect those morphological changes with kind of the molecular mechanisms that we can discover through sequencing? So these are kind of the three motivating problems today. I'll really talk to you about how to address the loss
of spatial context when performing single cell sequencing by discussing a tool called slide seek, which is enables genome wide expression profiling in tissues at 10 micron resolution. And a lot of this work is you know, this is a collaborative effort between my group and then Costco's my neighbor at the road. And the picture, big picture of six effectively lets you do, un-targeted single cell sequencing. And at the same time, it lets you know where those cells came from in your tissue. And how do we do this? Well, inside we deposit individually, barcoded 10 micron polystyrene beads in a mono-layer on a surface. And and the key is that in each one of these beads, there's millions of oligos nucleotides with kind of a similar structure, a PCR handle or constant sequence, a spatial barcode. What this is, is a clonal sequence
for each bead that's different among beads. And you'll see how we use that in a second. A unique identifier for counting transcripts and a TV handle for capturing my name. And what we do is we deposit these beads into a month later on a slide. And here's actually a cover slip. Here's a
40 millimeter cover slip. With each one of these white circles is a 3 millimeter diameter array with about 100,000 of these 10 micron beads on it. And because these beads deposited randomly, we then take a microscope and sequence the clonal B barcodes for each bead in the array. I won't go into detail exactly how we go do this, but basically we're trying to reconstruct the sequence of A s C s G s and t s that make up each barcode for each bead. And we do that by taking a series of images where each color of one of four colors represents each base. So at the end of the day, we get kind of the xy locations of each barcode. So I think one of the main advantages of these special
capture, spatial transfer approaches that are very easy to do once you have made the array here, we can once you make the array, it's very easy to the experiment. You can take fresh frozen tissue and resection it directly onto the array, a 10 micron section. Then we basically perform molecular biology protocols. That's very similar to single cell sequencing, such as reverse transcription and PCR. And we end up with kind of like a sequencing library. And the sequencing library is very much like what the libraries you would like you get in single cell sequencing for each bead barcode, you have a digital gene expression vector. So you end up a
matrix of a cross barcode that counts where the columns are each gene and accounts of the gene that we're seeing for each bead. But now, if you remember, we have the spatial location of each barcode so you can just match them up. So you match up kind of the sequenced xy locations of each bead and now you have a spatial location for each read in your sequencing library. So what is the data look like? This is kind of actually one of the first experiments we ever did with sensing data has the same structure as single cell data. And what we can do is we can apply the same similar computational tools such as dimensionality reduction and clustering. And
actually here we ran slightly on the mouse hippocampus, but in the very left, what we've done is we've just done unsupervised clustering and colored beads by the cell type they belong to. In the middle. We haven't performed any imaging. We just colored the beads on the array by which cell type they belong to you from the unsupervised cluster. And they'll vary. Right you actually have
an architecture of the tissue and you see that we reconstruct the tissue architecture. We also reconstruct the localized. of cell types, so the takeaway is that there's no microscope. In fact, you've turned the sequencer into a microscope instead of just taking one image or take many, many images across the transcript. And just some ideas about specifications. So he has very high spatial resolution, which we validated directly with a single molecule fish.
And then the resolution of the approach is about 10 microns the size of the beads. It also in some recent work, we've demonstrated that we can really improve the molecular sensitivity of the method about an order of magnitude to about 1,000 unique molecules per bead. This is similar to first generation droplet based single cell approaches, and I think this can be improved further just in terms of optimizing the molecular biology. But the nice thing is, is that because it's a spatial capture approach, it's actually quite scalable across many tissue types in organisms. And so we've applied it to many diverse mouse organs, a variety of human tumor specimens, as well as many developing embryo samples. And one of the things that we're focused on right now is how
do we perform large scale experiments in terms of batch effects and correlating these measurements across many sections in. And so the other thing that I think is very important with respect to all full transcript data is that we want to know how to two, there's two questions. One, we want to know how we can use single reference. Cell references that we've collected or birdie annotated cell types. And the second is that even though the beads are 10 microns, they're about the size of a single cell. There can be basically more than one cell captured per beat, about 1 and half, like most people have. One in some 30% of the beads have two cells. And we actually like to
know if we want to estimate the contributions from individual cell types to each beat on the array. And so we developed kind of a computational algorithm called robust cell type composition, which was recently published, led by Bob Dylan and in collaboration. Rafael is our lab. And this algorithm basically given a single cell reference in a spatial transcriptomics data can answer what combination of cell types and what proportion best fit each bead on the array. So now you can take a single cell reference and directly map it into space. And this allows us to do a couple of things. One, it allows for really high resolution cell type mapping because single
cell data sets are often much more diverse and now we can project very fine subtypes directly into the spatial data. So for here, for example, we've projected 27 interferons subtypes into a spatial data set of the massive campus. And here you can actually see kind of very fine layered excitatory neuron subtypes projected into the cortex. And you can reconstruct kind of the layered architecture. And actually we've demonstrated to work across many spatial transcription platforms, including bizim, although VCM obviously has much lower resolution than so. You can reconstruct the same type of spatial architectures and as well as imaging based transcriptase mix such as mahvish.
And so that is why it's really important to do this sort of cell type composition? Well, I think it's because there's been a lot of work in focus on detecting spatially varying genes from spatial transcripted data. And in general, these approaches have not been cell type of where. But I think what you actually want to know in your tissue is that you want to know how gene expression changes as a function of space within a cell type, as a function of tissue organization. And so using this sort of approach, we're beginning to be able to do that by discovering within cell type spatial variation gradients within a cell type. For example, these two genes, which are within 3 of the masticate campus or cellular neighborhood spatial variation like what? How old is gene expression change in one cell type as a function of who its neighbors are. For example, here we discovered specific genes which change as a function of their proximity to excitatory neurons. Just for the last one minute of my talk, I want to mention that in the sort of slightly captured platform can really be adopted to many different types of sequencing modalities, including DNA and in particular in cancer. We might be interested in collecting multiple McDade
data when we integrate DNA sequencing data, as well as RNA sequencing data to reconstruct kind of basically the clonal variation in combination with the transcriptomic variation. So we've adapted the sights you can raised to capture DNA through kind of a transposes based method in collaboration with Jason pronouncers lab and in actually it's easy to run serial section set serial sections because these beats are the same indexing. So now we can collect interior sections, DNA and RNA. And this allows us to do Novo identification of tumor, chromosomal, no aberrations as well as carnality, for example, assigning subclass by lineage, by their relatedness in mutations. And then what we can do is we can kind of quantify intrinsic and extrinsic factors of which contribute to gene expression. For example, if we have compared RNA and DNA data and spatial, we can examine basically like.
How does clonal assignment versus basically like environmental assignment affect gene expression, so here we can group basically genes by their various variants in gene expression, explained by either their clonal identity, which some clone of the tumor it belongs to, versus like cellular neighborhood affects one cellular or neighborhood effect that we look at with tumor cell density. And so we can assign genes which are associated with sub clone identity. And this might be associated with kinase and genes which are associated with, for example, tumor or immune density, which are more related to the cellular environment. So we think that's very exciting. I'm just out of time. So I just want to summarize that we've developed kind of very high throughput experimental approaches for high resolution spatial transcription profiling. I think the development of computational algorithms is extremely important at this point for looking at cell type projection, differential expression and interaction.
And we're really leveraging new tools and genomics to look at like Malcolm X and something. I didn't talk about receptor sequencing in the context of T cells in space. So thank you. And I'm happy to take any questions at this point or actually, I guess we're taking questions at the end.
Yes, thank you for the technology is amazing. I look forward to more development and new insights on this, so we'll take questions at the end. Now, is Miriam coming back? Nadia, so maybe we moved to the two to ourselves because people want from you. Stephen, Thanks for joining us today. Thank you, Michel, and thank you and Southwest for the invitation. And it's a great opportunity. I'm
so glad to be able to report our work on studying space, on architecture from prompting, chasing and amoeba. So as a conflict of interest disclosure, I'm actually Venter and a patent applied for a Harvard University related to mirvish. Our lab studies nuclear architecture. So I guess the first question is, what are the architectures? We use this word to describe the spatial organization of the tea and other nuclear components. So we know the genome is spatially organized in the folded
across multiple scales inside of the nucleus. First of all, DNA wraps around the histones to form these nucleosomes structures. And then on the other end of the spectrum, individual chromosomes actually occupy distinct nuclear space. And these are called territories. In recent years,
Thanks to advancements in sequencing technologies, we now know that at the large scale enough to several KB there are these topological satiating domains are that's whistling past that. There are often something loops, such as promoting has our looks and that has our father sort of these and B compartments and a compartment is enriched with the active chromatin and B compartment. This was inactive chromatin. Other sequencing technologies also revealed that there are specific regions of the genome that are associated with nuclear Islamiyah and there was nuclear Osler's and these are termed the labs and that is respectively. But these structures have
been shown to control many essential functions, not just transcription recognition, but also DNA replication, mutation rate repair recombination, and also, they show in triggering dynamics and changes during development, aging and many diseases, including cancers. However, there are still many unknowns regarding the nuclear market in all these biological processes, largely due to technical limitations, especially one key biological question is what is this treaty? Folding parts of commenting at length scales above the nucleosomes in single cells. So, from the tests to the AP compartments to come some territories, our understanding of the nuclear architecture is largely biology. So, these are essentially larger and larger blocks. How do we get the real speedy boarding pass of the single cells? And commission or sequencing technologies do not directly measure this really folding pass and often rely on population averaging. So,
they don't really answer this question. How much imaging techniques like the resolution and the multiplexing ability to reveal this really folding pass? To tackle this question, several years ago, we introduced this image-based charity genomics technique called the plummeting tracing that images and the pinpoints the positions of numerous economic loci along the same chromosome and the link them to review the folding parts of property. The challenge here is if you simply label so many genomic loci along the same chromosome, you don't know which one should be linked with which one. And also, due to the different element of light microscopy, there actually are all connected into one patch. So, you cannot even resolve the spatial following that. So many genomic loci
to tackle these difficulties. We designed this sequential imaging strategy, so we sequenced only label image and a pin point that you now make loci one after another. And you, all of them are a sequential image and the rebuild and we can link them based on all their anatomic map. So, this way we can really reveal and resolve this really boarding pass of individual chromosomes, single cells. So, this work really opened up this field called the image basis, really genomics. And there are many funding studies from many different labs, including several more works from our independent lab at Yale, applying this technique to different skills and to different model organisms and biological processes. And these are all based on actually multiple multiplexed
harmonization of fish. So basically, a library of that primary folks is simultaneously hybridized to the targeted genomic loci simultaneously. And these primary pops all have this overhead region that are unique to each dynamic locus of interest. And then one can basically sequentially hybridized bilabial on a secondary process and imaged them as we move the signal before the next round of hybridization. So, in such a way, all these dynamic loci one after another. But we also see that field is rapidly expanding to other varieties of technologies, technologies also such as the very exciting uses of sequencing technique that they just introduced that for me, that's where also, you know, our work, we one thing that we're focused on is to combine this a highly multiphasic DNA imaging. Was Holly multiplexed RNA and protein imaging all to a single integrated platform called MINA or multiplexed imaging nucleosome architectures? So, you mean that we achieved multi still and the multifaceted all imaging across many scales in mammalian tissue, in a cell type specific manner in single cells. So, this work, for example,
studying mosquito liver tissue we first look at the commenting folding organization across four orders of magnitude of genomic lens, all the way from promoting our interactions to cats to compartments to problems on territory. And then we asked the how are the folding differ among the diverse cell types in the field, however. And to answer that question, we image that 137 different species was a technique called RNA mirvish, which is a multiplexed RNA imaging technique among these RNA species. 55 of them are cell type marker genes. And we also label the cell boundaries. So, it can segregated individual cells and distinguish the cell types and
are the cell types of specific questions of nuclear manufacturers. We also try to answer, like how are the genome folding associated with expression changes? And to answer this question, the rest of the RNA species are actually from genes, from the choice to chromatin region. So, we measure their expression as well. And remember, we were interested in other nuclear components
as well. And in this where we label the cell nucleus and cell nuclei so that we can profile the association of all these genomic regions with the nuclear laminaria and study those architectures. And finally, because everything is done in situ, we naturally preserve the cell positions in this complex tissue and also the signaling molecule positions so we can study cell cell interactions. And the underlying signaling molecules in cell in this neighborhood
are showing several example images from the platform, from the same field of view, in the same cells I'm showing here, the permitting process is zooming in on two of them. It looks like this mouse, chromosome 19, folding trees. And then here are individual molecules of RNA and each one of them is color coded with over 100 subtle colors to show their different RNA species. Cell phones relabeling cell type Markaz, an expression cell type identification and also the volume of the nuclei and then hopefully olai. Next, I hope to show several example analyses of whether we can derive what kind of theological questions we can answer. Was this
high dimensional multi all mic technique and to data the first fetal liver based on prior knowledge. We already know that the cells express this very important and metabolic gene called SCV to the other cell types. Don't express it that much harder. If you look at the genome annotation, you'll see that near the fetal to promoter. There are several new cancer clusters. Which one or one of them interact with the city to promote her liver? Hepatocyte is and knowing using the media platform, we can basically group the permitting choices. And the choices of this
region separately, and we can show that to this cancer, interact with the city to promote her. So basically, we're not still permitting folding and can't discover cell types specifically promoting cancer interactions in complex tissues. And then we ask, what about the larger scale? How much enfolding, particularly how are the ab compartmentalization scheme differ in the different cell types in flavor? So, we know that the tax department comments are this AAA and B compartments in a whole chromosome and a compartmentalizing ritual with active promoting B compartmentalizing ritualist being active promoting. But these discoveries were made in homogenizes cell cultures. Now we have a complex tissue of so many different cell types. With MINA, we can profile the ab compartmentalization scheme and show that this scheme differs between the different cell types. We can also prove that in a single copy of the chromosome that the AAA and B compartments do physically exist as separated, prompting regions and the changes also the compartmentalization scheme. They are associated with C and expression changes. So,
Mina reveals cell type specific to become capitalization schemes that are associated with an expression changes, and that's the way of branching into the other nuclei architectures. We ask what is the relationship between a, B compartmentalization and the nucleus or lamina association of cremini regions where marrying the rate of the tax base will be associated with the nucleus? And then we compare that with the ab compromise or we see predominantly negative correlation between these measures. However, you see there are systematic changes between the different cells. So, these architectures they are specific to and the further show that
this is segregation of the compartments, they do not depend on the laminar or nuclear association. So, these results show that prompting compartmentalization. Our association of associations do not solely determine each other, and they may offer no controls to genomic functions such as transcription regulation in a cell type specific manner. And finally, we are asking, what are the cell, how the fetal liver cell types are arranged in space and we are projecting the type identities back until the image from these images that we can analyze which pattern of cell types are preferentially being neighbors with each other than expected, and that we can see the particular cell types in Paris are indeed preferentially being lavers. Some
of these discoveries are consistent with prior knowledge. For example, these red cells seem to surround macrophage and this is called a residual plastic island structure. However, some of the cell-cell interactions are enduring, for example, hepatocytes seem to be interacting with the janitors and this is indeed showing in our image as well. We ask her what her interactions may underlie this arrangement. And from our hundreds of RNA species that we profiled on the same image; we find a pair of legs. And as we set her leg and the kids
that are preferentially suppressed in these neighboring hepatocytes and everything else. So basically, mimic and potentially reveal cell type interactions. And the underlying molecular mechanism as a summary tracing allows to have spatial tracing that is really folding parts of quality individual chromosomes, single cells, I mean, enables multi scale integrative nucleophilic imaging in complex mammalian tissue and allows the discoveries of the Ormoc architectures in a cell type specific manner, studying of the relationship between architecture and transcription regulation, also studying of cell cell spatial interactions. This is because we now have one technique to image
them all and in the darkness of the microscope room, find them. And of course, our interest is not just limited to tissue development in the liver. For example, we have several works in the lab applying the technique to different types of cancers. And I hope to report those results in the near future. Finally, I'd like to thank my lab at Yale and my collaborators, professor Samuel , Sherman Weissman, the representative here where I naturally led by 2 talented with students and value. And thank you all for attending this presentation.
Thank you, Stephen, for bringing this data and technology from talking to us. Very interesting. I'm a big fan of mirvish, so I encourage people to put the questions in the chip box as well. So, we will have a discussion later on. So, I think we have Miriam back with us. Very nice to have you back. It's all yours now. I think you unmute. Yes, I did. I'm very sorry for what happened, so what can you tell me? When did when did you guys lose me? At the beginning.
The very beginning. So you can stuff. All right. OK, well, you have to learn to do that. Will try to be fast because I understand that we have some time to. We are on some timeline here. Can you see my screen? Yes, I am anxious now to do something wrong. I'm taking your time. OK all right. OK, so I just. Can you still hear me? You'll hear me. Right let me try to put these things OK. I'm just trying to see whether I'm going to do something
wrong. OK, so I was talking about my little compartment in criminal. I think you may have seen this. And specifically, I was emphasizing that macrophage is the largest human compartment in incompletions, but there is very little known about this compartment because most of the biology of has been obtained in Witold and there's very little knowledge of it, especially in human leashes. I don't know whether I've been through this, but this is an important slide that you guys
have to remember, because this is touching to be part of the textbooks. But we now know that there is a population of macrophage. We think they do things very differently, at least in some situations. There's this big tissue isn't macrophage compartment, that self-cleaning tissue that is present higher than any type of development that they are having printed by tissue who they killed in tissue repair, and that there is another population that are located mostly in response to inflammatory host. You guys, I'm still with you guys, right? Yes, you can hear me ok? I just want to make sure you know that, yeah, perfect. Then me. You hear me? Well, and the slides are OK. OK, I'll let you know.
All right, I then the other compartment that is located on in response to inflammatory. Q so two Melanesians, for example, a lot of inflammatory money could in fact very, very early progression. And we have observed the organization of my life compatriots awaiting the bone marrow that promote the good of mine. We put unitards into treatment. We put into Indonesian s and they also
are really part of the tumor. So, the first things we did to learn about the macrophage compartment is we go from this envoi used to profiling using sites, which allows us to look at the antibody profile of cells and a polygon using the next platform. And we first decided that we were going to spend a lot of time really profiling during my confinement of treatment. Na ve criminals. We are
focusing on criminals, including blankie and this is what I'm going to focus on today, but also HCC and where we are going to build this knowledge base and then build on top of it, then we do. We are doing some exercise to keep the tumor fitted with monotherapy, combination therapy, et cetera but today I'm going to talk about our efforts to provide the equipment, na f lesions and here. So, we use that knowledge to look for cells, express the same antibody and the same kocon. And we used to call them in cells, ILC T cells or macrophage, and we identify several macrophage Qualcomm. Then we look where they are distributed because here, we are a pathologist. We always isolate the tumor and the adjacent tissue and then we purify this compartment from both the tumor and adjacent that we are dealing with a different part of the tumor to look at heterogeneity of also of the regions. And then we do single cell suspension and decisive analysis. So,
the first analysis we do is look at distribution of all these, a molecular and viscously identify molecular and antibody profile of this minute cells in the adjacent tissue. And we spend a lot of time doing these then because we want to know the medical condition, of course, and the spatial distribution and the function of this compartment. We have to have access to some experimental model. So, we have a model that we imagine that we like a lot in the lab that was developed by tilo jaxa, which is this KP line which has extubated mutation and P53 deletion. And these lines, when injected, Kelly form these nice adenocarcinoma regions in the mouse and in the longer of the mouse. And the
first things we do, this profile is exactly the same findings. So, committed to look at my view of these solutions in mice. And then we use these species, and that is to look for PROC that are identical in mice and we identify for Pagan that are similar or we coalesce differently still that I showed you before. And we are going to focus on these four different people because we were able to do some fine tuning, especially using Google analysis. I don't have the time to go in deepening the analysis we performed, but we think that these are programs that are somehow engaged by 2 molecules, quite similarly in mice. And so not when we had this program. The first question
we ask is, what is the origin of these? My glove compartments? Are these macrophages? Are they derive from that blood circulating progenitors? And to do that, we use a faith mapping model that was developed by this crisis and that we had, in fact, a profile in these models. What we use is we use this one to calculate seventeen, which is early adult film precursors. We develop this mouse model that allows us to genetically label both the map Th17 cells conditionally upon tamoxifen injection. We conditionally label 17 and all the 17 progenies. And the genetic cases remain that they are completely untouched for the life of the animal. So, we feed them up the mice. And then when the mice are six months old, because it takes a lot of time to really fake my faithfully the hematopoietic, the right cells, we inject the tumor and then into tumor. We find the
blood cells from the blood cells and we look at the particular distribution of this mind. We put on label, on label. And what we found is there was one specific cluster that was never reached, the label cells, which we call cluster 1. And we these enable those to be densify these cluster of macrophages as the tissue adjacent macrophage. These are the tissue regions that are present in
the tumor. So, the. We do this identified Moscow that allows us to identify them using flow cytometry so that we can plug their function or also look at the official distribution. And what we found is that this caused a very high level of severe 169 high level of three to six. And this enables us to look at the spatial distribution of tissue into micro fit versus these adult bone marrow derived macrophages. And what we found is that the tissue Malkovich, surprisingly,
are always present outside the criminal. And this was also the same in human lesions. And then here we use these molecular analyses, we use the species. And then he says to see, OK, these macrophages resemble the tissue of macrophage that we analyzed using these faith mapping models in mice. So we think and they also were reaching to six. But also, when getting to 10, we use these two molecules to profile the tissue macrophage and we found are abundant outside the Melanesians.
That was surprising to us because the macrophage are part of the lung tissue and just want to make sure that you guys are here, OK, that you are here and and then so we then started to say, well, OK, so let's really look at the temporal distribution of macrophage. And we started now to the image, in fact, the composition, a different time location. And what we found is that these are the first, in fact, to interact with tumor cells. This is what I'm showing you here in red, that these are the tissue organisms macrophage. And Ingrid,
these are a KP cell leukemia expressing feel this is putting so that we can track them. So, these are the first interactions with micro fit. So, the experiments we do this weather is to probe with this a little intimate position. And here we use the reductionist model in which we call ourselves with tissue macrophage or multisided like micro fit, which we find are abundant in advance to Melanesians. And we ask whether they play different cording in promoting Tamil invasiveness. And strikingly, what we found is that the tissue macrophage where we put up inducing this common position where they could use the expression of an induced expression of some kind, would pull commonly used the formation of these branching carinae. So, they promoted really the capacity of macrophage to invade inside the jail.
I'm going quickly here because this paper has been published. I just want to oppose, in fact, the ability of this issue to induce, in fact, MTD antiproton. But what we also found is that this isn't macrophage. We are also interacting, especially only a joint application with regulatory cells. And they were deregulatory said that I'm showing here in red and these early in fact, that action was very intriguing to us and suggesting that maybe they were also inducing a regulatory sale. And indeed, what we see is that there is a first wave of T cells in Tamil that we've looked at. And then the cells are dampened. And I think this is something we think that this
injection of t like. But here is contributing to inhibiting this first wave of t relation to the next experiment we did is we depleted this macrophage without affecting the macrophage compartment. And we ask whether this will be used. In fact, to invasiveness, antiregulatory cell injection. And this is exactly what we saw, is that in the absence of tissue macrophage, Tamils have difficulty to invade despite the fact that the sieging of Tamil cells was not affected. And we also saw that there was a reduction of t- cell, but also the reduction in that direction of the functionality of T regulatory cells and an accumulation of THT cells. But this year, really,
if we now depleted ITSM later, there was no effect at all until closer. And this is because the serum at present, as I've excluded then from the tumor.So in order for the tumor to continue to grow in order, I mean, something happened and we are looking into exactly what's happening. We believe that the chemo then excluded because they are going to form somehow or build a lethal hormonal structure that's going to be harnessed by the tumor to into and are going to look to the side macrophage, which then become abundant in advanced stimulations. So, we started then to look at this one macrophage and indeed the we saw that in advanced nations monocytes macrophage dominate the tumor microenvironment and the outside the tumor microenvironment. However, this one says the right macrophages that we identified with our feet mapping methods are also quite heterogeneous. But there was one program that was specifically abundant in the tumor. And
these people can cause very high level of this walkable and now these two positive macrophages, we have heard about them mostly SRE. So, this is what I'm trying to do here is human lung cancer admissions. There is abundant of two positive macrophages. And this is a mouse, a lesion. There is also abundance of temporal macrophage. OK, so we had two positive macrophages. Sorry, before I
got here, I'm just showing you something that is positive macrophage at this particular cluster that we identified in human was also present in mice and in fact the outcome was quite identical. were many that were identical between mice and human. We also looked at the temporal accumulation of these two positive macrophages and what we saw that in contrast to the. That had
to be quite early due to an application that came to positive microphages recruited later during tumor growth. So now we had this to talk about, shared between mice and human, and they are abundant in advanced lesions. And we are going to look at how these two we have no tools to look at all of these come to fruition. But this is what they've been trying to tell you, is that we have four of these symptoms, mostly in the context of Alzheimer's disease. We've come to vain have been shown to be associated with increased receptivity to Alzheimer's disease.
We have also identified abundant to positive macrophage in colon cancer. In fact, in the paper that we published in Cell Malkovich had been found in liver. That happened also in adipose tissue with these patients. And there have been also shown to be abundant in experimental sarcomere cancellations by markkula and examined recently, in fact, in back-to-back papers published in Cell. And so, so this clearly it seems that these two positive things seem to be accumulating different inflammatory cues. Something that is common with all these people is the abundance of either fat tissue antigen gargle and indeed simple, easy scavenging receptors. So, the next
question we ask is whether it is applicable in the uptake of optimal cell degrees and to ask this question with really high macrophage. And by now we just take a mild marker in this case live in B and that are high because high level of GFP or low level of GFP. And then we look at their molecular whole. And what we found is that the positive and the they could come that I described to you was fairly strongly enriched in this GFP high macrophages that we isolated from a mouse, Tamil. Then we did now another experiment to further prove that this uptake of the cargo was inducing these Cocom. We divide, but not with the right macrophages, if you will.
And then we Fed them with expressing GFP. And what we saw is that these feeding process will induce this program to include the expression of time to if you want an MBA to eat. I have been up to all these people that seems always to be expressed together, but in mice and human associated macrophage. And now we see in the picture in macrophages that I have
cleared or captured apoptotic tumor cells. OK, so the next experiment we ask is whether what happened if we now try to delete them too, will we affect that had shown and that the deletion of time to reduce the progression of sarcomere consolidation's? So here we did the same experiment where we needed to fund the whole stuff from the microenvironment and injected the visa. I didn't look at my life and we saw that in the absence of a positive macrophage, that was a very strong prediction of tumor. And this was associated with the organization of the mildly puddicombe. We saw injection of this womb pushed up an expansion of one site. An extension of tissue has been to macrophages in blue, but also an extension of a specific subset of this one. And this seems to be very specific to this compartment, because this is two which are usually a month and was not affected by the deficiency. So, something was happening in the
myeloid compartment of deficient animal. Now, the problem is when we saw that there was such as tongji, a reduction at supercool, but the injection of them could also contribute to all this mildly changes that we observed. So, we did a cool experiment. I think when we look at it in mice with bone marrow cells that are mixed blood cells with a mixture of so proficient and efficient bone marrow cogenitor, which will give rise now to control proficient and deficient macrophage in the same compartment. Now there is no effect on millhauser. So we are going to purify come to a positive and negative macrophage because we label them differently genetically and we are going to compare their proconsul here that is identical. We are just looking at cell and testing all of time too. And what we saw was very exciting. The first we saw that positive and negative macrophage uptake, the same amount of tumor antigens. So the
cancer was not with insulating uptake, but we saw that to regulate regulated very strongly that he pulled. It's very strongly then the inflammatory production and expression, of course, stimulate the production of a lot of inflammatory monikered, including the production of five, 15 and 18, which are very important, could to activate and expand and says, I'm going to show you. They also come to whitecap was important for the plot of a scavenger scepter and also a lot of checkpoints, including this little bit for which we are very excited about. OK, so that's quite interesting. So now we go back and ask what happened in the environment of these two deficient sentiments and will come to deficient anyone? And what we saw is there was a very strong expansion that. So,
I showed you the reorganization of the microwave compartment. And what we also saw there was a very strong expansion of NK cells. And we are very excited about this because in cases are always depleted from the humiliation that we have seen that in human lung cancer. Also, there was a very strong depletion of cells. But also, we saw in that accumulation of activated cells, we also saw an accumulation of 80 cell, but also an accumulation of cells that in one are excess, also can be suggesting that these were affected the effect of the cells. So, the next experiment we do that is depleted to or 10 cases and ask whether we abrogated this beneficial effect. And this was indeed the case when we and not so much. So,
we are quite interested in this. It also seems that the beneficial effect was mostly due to the recruitment of a castellan, their occupation, and we also obligated these to see one accumulation here. So, this is quite interesting because first time we are able to expand in the compartment and expenditure one, and because of this result, we are starting our first in human study in advanced cancellations in patients at Saini. And this will be done with a small
biotech that have been acquired by Gilead biotech that was, in fact, from the coma. So, we are very excited about this. In conclusion, I'm just going to summarize my what I showed you. showed you the tissue relating to Michael promote jam rock invasive next to the injection of an individual kinmont position on an injection of only two legs that then contribute immunity. This is very
relevant for cancer institutions. We have shown similar results in so many years ago. There was a recent study showing that tissue macrophage also promoting this early invasiveness in the study seems to be leaving the property of this intellectual fate. This is very interesting to us because we have a very strong medicine that we can detect this early in site link institutions.
And we are going to now start a clinical trial to try to activate or deplete the tissue macrophage without operating on these lesions and see whether we can somehow use tumor. Also, I've showed you that the children are depleted from advanced lesions, which are dominated by macrophage, so that provide us with a way of targeting the right macrophage while sparing tissue isn't macrophage. We also have a lot of homeostatic property, including in the lung. They are the one that are
clearing the surfactant. They could be lung tissue integrity. So, if we don't have to target them, then let's don't do that. I showed you that is a very good target of these macrophage. I've also showed you the sensing of the bleed and use it. And these, for example, can be to the depletion
of encases in d.c., one from the Michelangelo momentum. And and that came to look to deficiency. But in collaboration with Michael Goodwin, we are also using the trip to the blockade, which led to very similar results where we can put on the middle landscape. I didn't show you the body of time to do that. The single celled sequencing of kintu deficient macrophage, you've seen them. So,
they start to be inflammatory. They start to produce inflammatory molecules, cuticle could and castell in documentation suggesting that we can reduce their ability to promote immunity. And we think that this is really a very strong Avenue to put in hands of responsible and cancer patients. These are my people. I know we are out of time. I'm not going to go through all the names here. Unfortunately, this is our group wearing our vaccine campaign t shirts,
and I'd be happy to address any question at the end of the talk. I hope I didn't go. I hope it won't be too long, and then with my presentation, I hope that you referred to me well. Yes, beautiful talk, Miriam, quite an exciting discovery. I'm glad that I was able to finish it. And did I go over the 20 minutes to much or not? I didn't
have a timer on it, but I think we are running out of time. We have a lot of questions in the list. So, I know that they need to go. Do you have time to take one question? Yeah, yeah, of course. I can think a couple questions right now. So, first question for you is, how do you quantify the density of tumor cells and stroma components in the tumor microenvironment from the data right in there? What we did was we used the transcript to make signatures to map the cell types into the space. And it's a spatial density based on the transcription signatures. You should stop sharing my slightly. OK, I should stop shooting for four hours, because if you have questions for each other, feel free to interrupt me to ask and share here.
OK well, I was going to ask my God that I would love to, I would love to. Now, look at my she said corruption in the government. And, yeah, we started to look at it. So I didn't. Yeah to purify my lord, my Lord. Then he said comportment. And what's clearly the kind of government is to
have an impeachment of Michael Vick. He said intellection that dominate the action. I said so can we. So to somehow forget the sequencing is the best way to go. Can we do some target sick where we just at least locate my said fact locally and then gloopy? Now, possible in today's world, there's many ways it depends on if you want to ask questions about so it depends on what your questions are like. Are you interested in the differential expression of genes as a function of the interactions? And you probably already have a list of targets from single cell, in which case it sounds like it's a good experiment for targeting imaging based methods as well, like mahvish or situ sequencing, which some of the technologies that we didn't I mean, that Steven talked about, I didn't get a chance to talk about. But that's great for targeted methods. If if you're interested in doing discovery of what the transcript like, what transcript terms are changing as a function of interaction, then you can use like untaken methods, like slides or speech, which makes it easier to inflation rate. So it's important to start having a sense of a single solution just because I'm sure to rate the genes are present everywhere.
So so give me a little bit of the resolution that you guys have now if let's say we want to go. I mean, I think I mean, Stephen can talk about more fish more than anyone, probably, but I think the resolution is very high, if you know what the targets are. Yeah, I started the collaboration with muffie Steven, as you know, so I'm looking forward to a very exciting and don't I like we have a molecule that I'm very excited about and they're all encompass all these monkeyface comportments that I described here today. So, I'm looking forward to this result.
You're the resolution. Actually, I have a question for you as well. So, you see the garfish have this a single molecule resolution. But I think for a lot of people, actually, they don't need to know the subsequent our distribution of RNA and wonder at a wholesale level spatial resolution is enough and technique, definitely. If you use the smaller spatial resolution, would it be better and better?
But I guess the texture of RNA will get lower and lower. So what's your estimate of how small the beat is optimal? Like a balance between a do or just your current size is already the optimal? That's a good question. I think probably optimal is, as you said, basically the capture rate scales with area. Right? like there's just less RNA. Ernest goes with area.
And so if you have the if you have the size of the beads, you get a four times reduction, but then you can dynamically aggregate. We aggregate the beads. If you have good computational algorithms, that's like a little bit like a different version of the segmentation problem. I actually think that probably the optimum lies at like 5 to 10 micron 5 microns maybe you already achieved. Essentially, it's close to where we are. I mean, we chose, what, 10-4 four? Good balance between those things. Yeah, but that is going to be impacted by how crowded places today are still there. Hey, did you see in the female microenvironments, will we find that, for example, where there is lymphoid aggregate resolution that you would require would be much higher than regions that are sparser? So, it's important to continue to think about that. The distribution is not equal and that solution is going to be affected by that. Mm-hmm
Yeah, OK, OK, I think we probably need to end this session, but it's great to see you guys already talking about this and then more and more questions are coming up. So, it's great to have biology and technology all coming together. And then we can bring cancer research on many other biological researches to the next level. And we also have many questions in the oral question, but I'll send those questions to our speakers. So that they can go through them and probably get back to you. So, with that, I think we have come to the end of the
session. We would like to say Thanks again to all of our speakers, Miriam Fey and Steven, for their engaging presentation. We are very grateful to our sponsor origin for the contributions that made these women all possible. We would also like to thank you, the audience, for tuning in today. If you missed anything during this, lamina, or you would like to listen to it again of the record. The recording will be available shortly, though. Still to come, if you have any comments about the women, all suggestions for future topics would love to hear from you by email at CP women women. Say thank you. Bye bye. Thank you. Thank you. Bye bye bye.
2021-08-12 17:11
