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Which are going to talk about the portfolio of special technology that broke a special biology, their applications and data analysis framework.
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So our first speaker is Doctor Sayani Bhattacharjee.
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She is managing the field application scientist team in the East region.
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She is with broker Spatial Biology for more than three years and she has experience in cancer biology and other special biology platform development.
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And she's going to talk about our portfolio of different technologies that we offer.
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And Sayani, please take it away.
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Thanks for the introduction, Ankush.
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Alright, hi everyone.
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My name is Sayani and today we are going to start off with talking about the different spatial multiomic technologies you can get from Bruker Spatial Biology.
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So before I start actually delving into the technology itself, let's talk about Bruker's vision and where we are coming from.
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From the very beginning, our goal has been to understand biology and cure diseases, and we know this can be done only by achieving high plex, so using a discovery approach.
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And you need to be able to look at FFPE tissues, which is what most of our clinical samples are found in.
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And with that in mind, we also have this idea that you cannot just generate data, you need to have an architecture to store it, to analyse it and for single cell spatial data also to segment it.
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So with these things in mind, our journey with spatial biology started back in 2019 when we were called NanoString and we came up with this platform called GeoMx, which is a profiler.
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We started with a small 84 Plex panel and then through the years we have only increased the Plex.
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You can see in 2021 we launched our whole transcriptome platform with GeoMx.
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In 2022, we launched a new spatial biology platform, CosMx, which lets you look at single cell spatial data.
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With CosMx, we have been making steady steps in increasing Plex.
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2023 we launched our 6000 Plex panel and this year in the summer we are launching our whole transcriptome panel.
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So whole transcriptome at a single cell resolution, which is groundbreaking and no one else in the market is doing that.
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With GeoMx and CosMx, you also have the option of looking at protein data along with your RNA data.
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So we are allowing you and the whole research community to look at multiomic data with your clinical samples to form discoveries to cure diseases and look for biomarkers.
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Today I will mainly be talking about GeoMx and CosMx.
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GeoMx, as I mentioned, you can look at the whole transcriptome.
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It's a profiler, so you're looking at different cell populations.
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You can have your tissue on a slide and ask questions like how are my cancer cells different from my immune cells?
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With CosMx you can look at single cell and get transcript and protein data at sub cellular resolution.
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With GeoMx you can also look at more than 570 proteins.
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We are actually expanding this to 1000 proteins.
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With CosMx you can look at 76 proteins along with your RNA data.
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Let's use a case study to understand how exactly we are using these technologies to answer our research questions.
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So we are going to use this example of lymph node tissue.
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We know lymph node is the site of B cell development and lymph node has a light zone, a dark zone.
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The dark zone is known to be the site of proliferation for B cells, and the light zone is where a lot of antigen presentation occurs.
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Now, single cell RNA seq has shown that there is also an intermediate zone that lives between the light and dark zones.
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But we don't really know fully whether there is any distinct biology that is associated with this intermediate zone.
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So we asked two different questions.
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The first question is there an intermediate zone with its own biology that is very different from the light and dark zones?
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And if so, can we find new spatial domains that have their own specific cell types and cell states using a single cell spatial biology?
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To answer these two questions, we used both GeoMx and CosMx.
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We used the GeoMx whole transcriptome RNA panel and for CosMx we used 100 Plex protein panel along with the 1000 Plex RNA panel.
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The question was to understand what the different spatial domains are, what dominant functional biology we see there, and what cell types in cell states are present.
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With GeoMx we drew 78 regions of interest capturing the different germinal centres.
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With CosMx, we actually imaged 1.4 million cells and when you look at the data from so many cells, it's going to be at least 1000 sub cell in the transcript, each cell which is going to be so much data for you to look at.
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The GeoMx data revealed that actually there are three distinct zones that you can see with within these germinal centres.
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This ternary plot here shows you the pathway enrichment within these zones for different pathways.
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The more it shows up close to the corners, the higher the enrichment score for that particular pathway.
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For the light zone you can see there is this costimulation by CD28 that is showing up.
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The dark zone shows pathways for B cell proliferation and the intermediate zone shows some new pathways which actually don't play a role in the other zones, for example interleukin 18 signalling.
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When we plot the pathway genes, for example for CD28, we can see the light zone regions have higher Z scores for these genes as we would expect.
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When we go over to CosMx data, we can now start asking questions like OK, I know what the biology is in these different zones, but how am I getting that?
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Which cells are playing a role in it and how are they presenting this biology?
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So what we found is that these light zone B cells show up along with follicular T helper cells and we could see that by co-localising together, they are actually using CD86 to form the ligand receptor interaction, which is giving us the biology that we are experiencing.
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Again, that is what we're seeing here.
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So in a way, if you are thinking about how to integrate GeoMx and CosMx in your research, you can think of it in this way.
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GeoMx is telling you what happened, CosMx is telling you who did it and how was it done.
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Using this approach, we basically could structurally profile 78 regions of interest.
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We have data from 1.4 million single cells and we could deconvolute cells into 27 different types.
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We found that each of those 3 zones have their unique spatial transcriptome, and we could also find what receptor ligand interactions were driving this B cell maturation in the three different zones.
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But this is not where our collaboration with the scientific community ends.
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We wanted to keep pushing the boundaries.
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We don't just want to stop at 6000 genes with CosMx, we wanted to open the door for discovery and introduce the Whole Transcriptome panel for CosMx.
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So this is a bio archive paper which is available for you to look at, where we imaged 6 different sample types with the CosMx Whole Transcriptome panel which will be available commercially this summer.
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You can see these are all FFPE tissues, so even if there is some degradation we're good with it doesn't really matter.
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More than 2.7 million cells were imaged over here, which corresponded to 5.4 billion transcripts.
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That is a huge amount of data.
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This was around 1550 transcripts per cell, which was around 900 unique genes.
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And all of this data is available publicly.
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So if you want to play around with the data, you have that option.
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We have been working with several collaborators for this panel and I'm going to talk about how exactly this panel is going to be a paradigm shift in scientific discovery.
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So let's take this breast cancer tissue for example.
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We are going to go over this tissue and answer some questions with it.
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We can see some of these different pathways, how the heat map for them is actually mapped back into the tissue.
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When we are getting these pathways scores from GeoMx for example, we see a normal heat map, but when you are going with CosMx whole transcriptome, you can get all the pathways that are possible from one tissue mapped back onto it.
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Here we have pictures for only four, but you can actually get 2751 of these.
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And what does that mean for your data?
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So here we can see this is what the actual morphology of the breast cancer tissue is like.
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There is an invasive front and there is also a primary tumour that we can see towards the back.
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So we are going to take two different domains, two different spatial domains in the invasive front and see what kind of biology we can infer from it.
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With most other panels, how you approach single cell biology is you start with the different cell types, you figure out what cells are present in your sample and then you try to guess what pathways they might be triggering and infer the biology.
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But when you're looking at this whole transcriptome data, you no longer need to infer the biology.
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You can think of this as a top down approach where you are actually looking at the interactions directly and then you can go back to cell typing and understand, OK, this is how this particular reaction happened.
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So the Tan domain here shows you there are several pathways that are involved in proliferation in this invasive front and then you see this light blue domain which is basically all metabolic pathways that have to do with cancer aggressiveness.
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So this completely makes sense.
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You can also find out which genes are either enriched or depleted in these invasive domains, and you will find genes that are not part of any other commercially available panel.
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So imagine anyone gene that you find to be depleted or enriched in a domain like this.
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It's a novel discovery in its own and you can write a whole paper on just that.
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You can also find some very unexpected biology.
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This is an example of a colon tissue that was used for IBD research and the researchers actually found olfactory receptor signal in this tissue, which was very surprising.
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And then they found out that this olfactory receptor can actually sense various metabolites from bacterial byproducts, which was a very novel finding.
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CosMx can also be used with cells in suspension for example with STAMP.
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There's a bio archive paper on this which you can read.
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STAMP stands for single cell transcriptomics analysis and multimodal profiling using imaging.
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Yes, I memorised that.
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It's a very cool technique where you're essentially just taking your cells in culture and stamping them on a slide and you're using different imaging based single cell spatial platforms to look at the data.
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And this helps you to avoid sample loss and extra costs associated with single cell sequencing.
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CosMx also lets you look at RNA and protein together.
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This is an example of lung cancer tissue where we can see tumour in the top region here and here.
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Now this is the RNA data, which is pretty broad and sparse, but if we want to drill down into it and understand what's happening with the sample, we can start looking at the protein data.
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We see fibronectin is basically creating a barrier over here.
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Then if we look at smooth muscle actin, we can see there is another barrier that it's creating with the tumour tissue.
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Then we can look at some protein markers that actually help with immune suppression.
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And as we go forward, the whole data kind of starts to make sense.
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So you're not just getting the broad sparse idea, but you're starting to understand how the transcriptome and the proteome are providing this whole picture together.
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But data analysis remains a big part of spatial biology research in general.
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You cannot just generate billions of transcripts worth of data and not have an architectural structure to understand it and analyse it.
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So we provide this spatial informatics platform called AtoMx, where you can visualise your data as well as analyse it.
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Your CosMx data will directly go into AtoMx where we have the option of running different analysis pipelines.
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You can segment your data over there and we actually use a very image first approach when analysing our data, which means you start with the whole image that you have scanned, you look at different fields of views and start analysing it on the image itself.
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Our segmentation approach uses a combination of the nucleus and cell boundary model, and you always have the option of selecting segmentation markers which are particularly relevant to your tissue type so that you can draw these boundaries as accurately as possible.
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We also provide several data analysis resources, and we just want our customers to succeed.
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So finally, to sum it all up, our focus has always been to find cures for diseases and discover new biology.
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We're always pushing the paradigm when it comes to plex in spatial biology.
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And we will always continue to support the research community in different analysis options and architecture.
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And that's all I have.
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Thank you.
