Leica Microsystems
Duration: 14:51 Min
Exploring the Tumor Immune Landscape with Multiplexed Imaging & AI-Powered Spatial Analysis
Transcript
0:01 Hi everyone, I'm Vasundhara. I'm representing Leica Microsystems and today we'll be talking
0:07 about exploring the tumor immune landscape using multiplexed imaging and AI-powered spatial
0:14 analysis. So spatial biology allows us to study a variety of cellular landscapes
0:21 across different dimensions and it has applications in various fields in molecular biology.
0:26 So for example, within neuroscience that you see here, we can look at different cell types,
0:32 how they're organized within the brain for example, and then within the space of cancer,
0:38 we can look at how different immune cells interact with the tumor to create,
0:48 to really go beyond what a typical digital pathology image looks like and create more
0:55 of these functional atlases to finally generate data across various different landscapes from
1:05 subcellular to micro-anatomical structures. And each layer can be used to inform the next,
1:13 either through this bottom-up or top-down analysis method. And for the purpose of this talk,
1:18 we'll be focusing more on the tumor immune landscape side of the things.
1:25 So we know that it is important to characterize the tumor immune microenvironment, but it contains
1:33 distinct immune cell types. So for example, there's immunosuppressive cell types within the TME
1:41 and at the same time, these exist in combination with the anti-tumor immune cells. So they kind of
1:48 protect us from the, against the tumor cells. So naturally, since there's such a large variety
1:55 of cell types, it requires a multiplexed approach to really study such diverse cell types in order to
2:06 develop effective cancer treatments. And that's why we introduced CellDive multiplex imaging
2:13 solution to interrogate such intricacies of the tumor immune landscape. So it allows us to
2:20 perform hyplex imaging within whole tissue sections at a single-cell level, using an iterative
2:27 staining and imaging approach. And the key features are that you can look at more than
2:34 60 biomarkers within the same tissue. It's tissue-preserving, so you can always go back
2:41 and add as many biomarkers as you want. And at the same time, you get really high signal-to-noise
2:48 ratio because of the calibrations that we do, as well as there's like lesser batch-to-batch effects
2:58 because of the increased intensity. So what is CellDive? This is like a brief overview.
3:06 You start with your FFPE tissue section. You identify which area that you want to image,
3:15 either it's the whole tissue or a specific region of the tissue. And that's where the CellDive
3:20 process starts, where we start with biomarker staining. So we add four different antibodies,
3:27 we image them, and we inactivate those antibodies and then capture the autofluorescence that can be
3:36 removed. And then with each iterative round, you keep adding four biomarkers in each cycle and keep
3:44 repeating the cycle until you have the final data set that can be analyzed using our analysis
3:49 software, which I'll go over in the next step. So what does a typical data set look like?
3:56 You start off with DAPI, look at the nucleus. We add, let's say this is a T-cell marker. We look at
4:03 immune checkpoint markers. So these four would be within round one. And then you can add
4:13 more antibodies, keep layering it. You can look at endothelial cells, macrophages,
4:19 proliferation markers, that's round two. And then more tumor evasion markers,
4:25 Vimentin, CD11C, GLUT1, which is a hypoxia marker. So you can keep, essentially keep adding as many
4:32 markers as possible, which are important to your study. And once we have the entire,
4:43 this is like the entire tissue, this is what the image would look like. It's basically like a map
4:48 of your tumor tissue here. We're showing a colon cancer tissue from the CD26 wild type
4:58 syngeneic mouse tumor. And we're looking at these many biomarkers that are segregated by the kind of
5:08 phenotype that they are annotated to. And what's really interesting is that if we zoom in,
5:15 you can really appreciate the single-cell resolution that you can achieve with the
5:20 CellDive hyplex imaging. And another thing is that the same, pretty much the same set of
5:28 antibodies and the imaging calibration settings can be applied across different tumor types.
5:34 So it's showing colon, lymphoma, breast. And now we come to like a more important question. So
5:43 here you're looking at cytotoxic and helper T cells in this field of view. And you can also
5:50 look at other cell types, like B cells within the same field of view. But it gets really complicated
5:57 when we're, let's say, looking at more than 25 biomarkers and how they interact with each other,
6:04 especially within the whole tissue level. So that's where we ask some of these questions,
6:10 like what are the molecular signatures that are associated with the TME? What are the
6:16 relationships between each of these biomarkers that we just saw? And what are the spatial
6:22 interactions? So what is the geographical information that's carried by these markers?
6:28 And that's where our AI analysis platform comes into play to really answer some of these key
6:36 questions. So our AI analysis software, it's called AVIA, which stands for AI Visualization
6:45 and Image Analysis. So you start off with your original image. And the first step with any kind
6:51 of multiplexed analysis is detecting individual cells, so really assigning them into individual
6:57 objects. And what the result looks like using our cell detection recipe is you get those individual
7:08 outlines of the nuclear and the cell membrane by utilizing all the channels that are available
7:15 within our data set. And then the second step is looking at what are the different phenotypes that
7:26 are assigned to each of these cell types. So that's called phenotyping. And that's where,
7:33 like here, what you can see is the yellow cells are immune cells and the red ones are the blood
7:39 vessels, endothelial cells. And with our AI-assisted technique, we can immediately get like a zero-shot
7:49 solution where it assigns the marker to each of these cell types. And then the question becomes,
7:56 okay, we can do this for, let's say, one marker. Sure, that's easy. But when we're looking
8:02 at all the different markers, we want to know what are their interactions with each other.
8:08 And that's where the unsupervised clustering that AVIA can do comes into play. So in here,
8:17 you see two of the tumor types. And we can look at what are the different cell populations based on
8:27 what all markers they co-express. And what's really interesting here is that we know these
8:33 T cell clusters and these two cancer types that not only express specific immune checkpoint markers,
8:41 but also express these immunosuppressive mechanisms that might be impacting T cell function based on
8:48 the regulatory markers that they express, like FOXP3 and IL-2RA. And it really indicates, like by
9:04 not just looking at like one cell type, we are also looking at how these cell types interact with
9:09 other immune clusters, like in this case, they're interacting with dendritic cells in cluster 5 and 9
9:17 on the right figure. And really, I think the changes between these two tissues for the same cell
9:24 population really indicate the heterogeneity that exists within distinct tissue types for tumor.
9:31 At the same time, within the same samples, you see these specific myeloid clusters that
9:38 express M1 and M2-like functional markers, along with immunosuppressive mechanisms
9:45 with PD-L1 expression. And what's interesting is that both these myeloid clusters and these two
9:52 different cancer types express GLUT1, which is a tumor hypoxia marker. So that really
10:00 got us interested in looking at what are the spatial expression patterns of GLUT1.
10:07 So GLUT1, wherever it's enriched, those are the tumor cells that are deprived of oxygen and
10:14 nutrients, yet they survive. They provide some resilience to the tissue. And we are interested
10:23 in seeing, are there specific cell types that might be enriched in regions that have high
10:31 GLUT1 expression versus the ones that don't? So that was really the question. And that's,
10:37 I think, kind of the beauty of more of the spatial analysis to really know if there's
10:42 any location-based preferences for some of these markers. So in order to answer that,
10:51 we can look at, we can cluster cells by the level of expression of markers. So here we're
10:59 looking at three different clusters, a red indicating high expression, green indicating
11:06 medium, and then blue is little or no expression. And then we can look at what are the immune cell
11:14 types that are immune or like functional cell types that are enriched in each of these clusters.
11:20 And what's interesting here is that the high GLUT1 regions tend to express
11:28 a lot of these myeloid markers, especially
11:34 tumor-associated macrophage markers like CD206. And we took a deeper look at some of these
11:45 specific markers. Here we're looking at the spatial relational analysis. So it's a heat
11:52 map of distances on the right where the red indicates cells that are very close to the
11:58 GLUT1 enriched region. And it kind of shows that these tumor-associated macrophages that are noted
12:06 by CD206 and CD11b expression, they kind of create these hubs at these tumor hypoxia regions,
12:17 which is extremely interesting. And not only can we do this, we can also go beyond that,
12:23 really look at what are the percent expression levels of some of these interesting cell types
12:30 like T cells that express regulatory phenotypes as well as dendritic cells, M2-like macrophages,
12:40 and B cells. And we clearly see that there's an increase of specific cell types within the GLUT1
12:46 rich regions versus the poor ones, which we can also confirm with some of these images that we see
12:55 based on our CellDive imaging results. And in conclusion, if I have to summarize,
13:04 the take-home message would be that we're really able to capture the tumor immune landscape using
13:11 this multiplexed approach. And we can use metabolic markers like GLUT1 to look at how
13:18 they regulate immune cell activation within the TME, like I showed with the clustering analysis.
13:26 And not only that, we can also look at the complex tumor immune interactions
13:31 within the whole tissue using an unsupervised clustering on AVIA.
13:38 And finally, we can note the enrichment of specific immunosuppressive cell types like
13:45 tumor-associated macrophages or TAMs within the GLUT1 positive
13:51 regions, and really that such spatial expression patterns may contribute to therapeutic resistance.
13:59 It's important to know how the cells are localized within the tissue.
14:07 And I would like to thank our Leica team at the Boston Innovation Hub at Walton,
14:15 as well as CST for some of the tissues that they provided for this study.
14:20 And if you have any kind of questions and you want to learn more about CellDive or any
14:26 of the spatial analysis studies, Jade and Natasha here would be really the best people to reach out
14:36 to. And so far, we really looked at the imaging-based methods for molecular profiling.
14:44 And in the next talk, we will learn a little bit more about cell and gene therapy. So I'm
14:51 just going to take the questions after the next talk and move on to the next one.