Leica Microsystems
Duration: 14:51 Min
Exploring the Tumor Immune Landscape with Multiplexed Imaging & AI-Powered Spatial Analysis
Transcript
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Hi everyone, I'm Vasundhara. I'm representing Leica Microsystems and today we'll be talking
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about exploring the tumor immune landscape using multiplexed imaging and AI-powered spatial
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analysis. So spatial biology allows us to study a variety of cellular landscapes
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across different dimensions and it has applications in various fields in molecular biology.
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So for example, within neuroscience that you see here, we can look at different cell types,
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how they're organized within the brain for example, and then within the space of cancer,
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we can look at how different immune cells interact with the tumor to create,
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to really go beyond what a typical digital pathology image looks like and create more
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of these functional atlases to finally generate data across various different landscapes from
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subcellular to micro-anatomical structures. And each layer can be used to inform the next,
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either through this bottom-up or top-down analysis method. And for the purpose of this talk,
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we'll be focusing more on the tumor immune landscape side of the things.
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So we know that it is important to characterize the tumor immune microenvironment, but it contains
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distinct immune cell types. So for example, there's immunosuppressive cell types within the TME
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and at the same time, these exist in combination with the anti-tumor immune cells. So they kind of
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protect us from the, against the tumor cells. So naturally, since there's such a large variety
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of cell types, it requires a multiplexed approach to really study such diverse cell types in order to
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develop effective cancer treatments. And that's why we introduced CellDive multiplex imaging
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solution to interrogate such intricacies of the tumor immune landscape. So it allows us to
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perform hyplex imaging within whole tissue sections at a single-cell level, using an iterative
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staining and imaging approach. And the key features are that you can look at more than
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60 biomarkers within the same tissue. It's tissue-preserving, so you can always go back
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and add as many biomarkers as you want. And at the same time, you get really high signal-to-noise
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ratio because of the calibrations that we do, as well as there's like lesser batch-to-batch effects
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because of the increased intensity. So what is CellDive? This is like a brief overview.
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You start with your FFPE tissue section. You identify which area that you want to image,
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either it's the whole tissue or a specific region of the tissue. And that's where the CellDive
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process starts, where we start with biomarker staining. So we add four different antibodies,
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we image them, and we inactivate those antibodies and then capture the autofluorescence that can be
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removed. And then with each iterative round, you keep adding four biomarkers in each cycle and keep
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repeating the cycle until you have the final data set that can be analyzed using our analysis
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software, which I'll go over in the next step. So what does a typical data set look like?
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You start off with DAPI, look at the nucleus. We add, let's say this is a T-cell marker. We look at
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immune checkpoint markers. So these four would be within round one. And then you can add
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more antibodies, keep layering it. You can look at endothelial cells, macrophages,
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proliferation markers, that's round two. And then more tumor evasion markers,
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Vimentin, CD11C, GLUT1, which is a hypoxia marker. So you can keep, essentially keep adding as many
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markers as possible, which are important to your study. And once we have the entire,
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this is like the entire tissue, this is what the image would look like. It's basically like a map
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of your tumor tissue here. We're showing a colon cancer tissue from the CD26 wild type
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syngeneic mouse tumor. And we're looking at these many biomarkers that are segregated by the kind of
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phenotype that they are annotated to. And what's really interesting is that if we zoom in,
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you can really appreciate the single-cell resolution that you can achieve with the
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CellDive hyplex imaging. And another thing is that the same, pretty much the same set of
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antibodies and the imaging calibration settings can be applied across different tumor types.
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So it's showing colon, lymphoma, breast. And now we come to like a more important question. So
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here you're looking at cytotoxic and helper T cells in this field of view. And you can also
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look at other cell types, like B cells within the same field of view. But it gets really complicated
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when we're, let's say, looking at more than 25 biomarkers and how they interact with each other,
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especially within the whole tissue level. So that's where we ask some of these questions,
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like what are the molecular signatures that are associated with the TME? What are the
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relationships between each of these biomarkers that we just saw? And what are the spatial
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interactions? So what is the geographical information that's carried by these markers?
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And that's where our AI analysis platform comes into play to really answer some of these key
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questions. So our AI analysis software, it's called AVIA, which stands for AI Visualization
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and Image Analysis. So you start off with your original image. And the first step with any kind
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of multiplexed analysis is detecting individual cells, so really assigning them into individual
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objects. And what the result looks like using our cell detection recipe is you get those individual
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outlines of the nuclear and the cell membrane by utilizing all the channels that are available
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within our data set. And then the second step is looking at what are the different phenotypes that
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are assigned to each of these cell types. So that's called phenotyping. And that's where,
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like here, what you can see is the yellow cells are immune cells and the red ones are the blood
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vessels, endothelial cells. And with our AI-assisted technique, we can immediately get like a zero-shot
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solution where it assigns the marker to each of these cell types. And then the question becomes,
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okay, we can do this for, let's say, one marker. Sure, that's easy. But when we're looking
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at all the different markers, we want to know what are their interactions with each other.
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And that's where the unsupervised clustering that AVIA can do comes into play. So in here,
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you see two of the tumor types. And we can look at what are the different cell populations based on
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what all markers they co-express. And what's really interesting here is that we know these
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T cell clusters and these two cancer types that not only express specific immune checkpoint markers,
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but also express these immunosuppressive mechanisms that might be impacting T cell function based on
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the regulatory markers that they express, like FOXP3 and IL-2RA. And it really indicates, like by
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not just looking at like one cell type, we are also looking at how these cell types interact with
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other immune clusters, like in this case, they're interacting with dendritic cells in cluster 5 and 9
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on the right figure. And really, I think the changes between these two tissues for the same cell
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population really indicate the heterogeneity that exists within distinct tissue types for tumor.
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At the same time, within the same samples, you see these specific myeloid clusters that
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express M1 and M2-like functional markers, along with immunosuppressive mechanisms
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with PD-L1 expression. And what's interesting is that both these myeloid clusters and these two
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different cancer types express GLUT1, which is a tumor hypoxia marker. So that really
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got us interested in looking at what are the spatial expression patterns of GLUT1.
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So GLUT1, wherever it's enriched, those are the tumor cells that are deprived of oxygen and
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nutrients, yet they survive. They provide some resilience to the tissue. And we are interested
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in seeing, are there specific cell types that might be enriched in regions that have high
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GLUT1 expression versus the ones that don't? So that was really the question. And that's,
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I think, kind of the beauty of more of the spatial analysis to really know if there's
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any location-based preferences for some of these markers. So in order to answer that,
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we can look at, we can cluster cells by the level of expression of markers. So here we're
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looking at three different clusters, a red indicating high expression, green indicating
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medium, and then blue is little or no expression. And then we can look at what are the immune cell
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types that are immune or like functional cell types that are enriched in each of these clusters.
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And what's interesting here is that the high GLUT1 regions tend to express
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a lot of these myeloid markers, especially
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tumor-associated macrophage markers like CD206. And we took a deeper look at some of these
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specific markers. Here we're looking at the spatial relational analysis. So it's a heat
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map of distances on the right where the red indicates cells that are very close to the
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GLUT1 enriched region. And it kind of shows that these tumor-associated macrophages that are noted
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by CD206 and CD11b expression, they kind of create these hubs at these tumor hypoxia regions,
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which is extremely interesting. And not only can we do this, we can also go beyond that,
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really look at what are the percent expression levels of some of these interesting cell types
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like T cells that express regulatory phenotypes as well as dendritic cells, M2-like macrophages,
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and B cells. And we clearly see that there's an increase of specific cell types within the GLUT1
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rich regions versus the poor ones, which we can also confirm with some of these images that we see
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based on our CellDive imaging results. And in conclusion, if I have to summarize,
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the take-home message would be that we're really able to capture the tumor immune landscape using
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this multiplexed approach. And we can use metabolic markers like GLUT1 to look at how
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they regulate immune cell activation within the TME, like I showed with the clustering analysis.
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And not only that, we can also look at the complex tumor immune interactions
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within the whole tissue using an unsupervised clustering on AVIA.
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And finally, we can note the enrichment of specific immunosuppressive cell types like
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tumor-associated macrophages or TAMs within the GLUT1 positive
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regions, and really that such spatial expression patterns may contribute to therapeutic resistance.
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It's important to know how the cells are localized within the tissue.
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And I would like to thank our Leica team at the Boston Innovation Hub at Walton,
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as well as CST for some of the tissues that they provided for this study.
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And if you have any kind of questions and you want to learn more about CellDive or any
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of the spatial analysis studies, Jade and Natasha here would be really the best people to reach out
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to. And so far, we really looked at the imaging-based methods for molecular profiling.
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And in the next talk, we will learn a little bit more about cell and gene therapy. So I'm
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just going to take the questions after the next talk and move on to the next one.