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Screen Life Sciences Dr Sumeer Dhar received his PhD in clinical pharmacology from the Uppsala University in Sweden and did his post-doctoral training at the Duke University Medical Centre in the United States.
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Today he's going to be talking about the leveraging screen imaging technology to enhance better evaluation of 3D ex vivo disease models.
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Stage is yours?
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OK, so this is the next.
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Thank you for introduction and thank you everyone for coming here.
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And it's been so far a really nice conference and we have seen over the years how 3D assay systems coming from non chromogenic assays to single spheroid based and then organoid rhetoric models, immune competent models and vascular models.
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So how these assays have developed and how this development has also increased the challenges with regards to complexity.
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And that also means that we need a better imaging systems or measurement systems to really make a sense of all the data that, excuse me, comes out.
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So over the next 20 minutes, I'm just going to walk you through the imaging technology that SCREEN produces and the solutions that we produce coming from drug discovery screening and drug efficacy based assays.
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So SCREEN is, I would say rather not so known company in the imaging section at this point.
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So the history, although the company's history goes back 100 years, founded in Kyoto and currently almost 6000 people worldwide and the major aspects for the SCREEN has been coming from semiconductor business.
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So 70% of the market share comes through the semiconductor business, graphic displays and also graphic arts equipment.
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So now the question is that we are coming from the semiconductor business, what does that got to do with the life sciences?
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So that's exactly the point.
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So we are located in Kyoto and also have our labs in Kyoto University.
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So they have had a lot of work going on in the oncology and stem cell research.
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So that's how the idea started from there that how could we implement the imaging expertise from screen into developing more 3D imaging systems.
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So that's how we evolve, I mean, since the last, I would say 10 years.
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So another factor what we did, we went more like an unconventional way.
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So rather than going from 2D imaging to 3D, we set up the 3D imaging platform and then incorporate the 2D imaging.
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So this is our the recent, the recently launched imaging system here.
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So we have XYZ adapted technology, it's one also one of the fastest imaging platforms.
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We use omnifocal projection of the 3D cultures and also the one of the key points is the Z stacking and I will go through these points in the next slides.
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And we also have another system which is more optical coherence tomography based instrument using infrared laser technology, which we say is do say is a true imaging platform.
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So I'll also touch upon that a little bit in the later talk.
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So what does NX do?
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So I just told mentioned about the omnifocal projection.
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So what we can do, since it's a bright field imaging platform, so we do when it comes to organoid, for example, organoid samples.
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So we have these objects are organoid sitting anywhere in the well.
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So how would we make sure that we have all the organoids in focus?
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So what we do is in the Z stacking we make slices.
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So in this particular example we made 31 slices.
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So this is how it scans each slice and what you get output is the organoid picture.
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So this is the 2D projection.
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So now you see here.
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So all these organoids are in focus.
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So we are not going to lose any information and we and also be able to do more robust analysis.
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So again to reiterate on this example.
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So this is the single layer, and you see these intestinal organoid.
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So you have some which are in the focus and some which are out of focus.
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But when we do the Z stack, so you have all these organoids in focus and indeed intestinal organoid look very nice.
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They are roundish, but you know, when it comes to the more cancer, they are organoid, they won't look as roundish.
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But nevertheless the basic message is that we can get all these guys into focus.
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And the second part also, what's really striking is that we have the concentric lens capability that means we can do the whole valve image.
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And also, we are not concerned about the meniscus effect.
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So you see here we are, it's, we can image these objects which are lying next to the edge of the well, but we can pick those up.
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And so you see on the left side the raw brightfield image.
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And then we also have developed the deep learning technique.
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And this deep learning technique again goes back to the here the history where we needed to have algorithms to really pinpoint artefacts in semiconductor chips.
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So these artefacts which are as even smaller than the wells.
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So we applied and implemented that in the last time in the 3D imaging and also the labelling.
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So as you see here, these are all labelled and then can be extracted and further analysed.
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So here I'll just quickly show you an example using cystic fibrosis model that we've been working with one of our collaborators in back in Japan.
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So I mean we all are aware that cystic fibrosis is one of the toughest diseases and not many treatment modalities are available.
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So what they have done, they have developed certain 3D model platforms so to be able to conduct the drug development and also find out more potentially active drugs.
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So the whole process how they go about it that right from the stem cell general isolation and then going further as you see in the workflow and once the organoids are generated and the next part is to plate these and do the assays.
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So they had couple of issues or challenges.
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So they've been utilising forskolin induced swelling assay which is now a well-known and well accepted in the clinical settings also.
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And so they used fluorescence based methodology.
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So as you see here, these organoids are stained and then treated or without forskolin or with forskolin and then incubated and imaged using the fluorescence based method.
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So the challenge that they had here was that they found that there was always uneven staining in this organise and also there was a bias in like once you have the cut off determination.
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So you it also poses lots of challenges.
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Then the second cell what they wanted to do figure out whether they can really this is not from the cell images.
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This one of their from microscopes, the image.
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So the whole the reason I'm like emphasising here is that they want to see whether they can do more label freebase assays and also apply the deep learning segmentation methods to their cultures.
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And so what they did in the next slide.
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So this is like a just in nutshell.
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So you know you how you scan the plates and then you have the brightfield image here.
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Then you do the recognition pattern, identify and label these and prepare the training data set and then apply that training data set into the real time data.
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So using this approach the again going back to the forskolin.
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So FIS assay.
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So here we have the experiment conducted in time dependent manner like you see from zero to 27 hours and then these organoids were also treated with forskolin.
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So on one hand here forskolin and also the CFTR inhibitors.
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So you see there's also and could also the these are measured post the treatment.
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And then also you could see the inhibition in the swelling using the CFTR inhibitor.
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So this is the brightfield image here.
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And then when they applied the segmentation process, so there you could see that it was it could correctly identify these objects and then do the further analysis based on swelling, increase in swelling or decrease in the swelling when the CFTR inhibitors were added.
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So again here, so they used isogenic IPSC derived organoid as you see here on the top and then also the cystic fibrosis derived organoid using the DMSO as a control and the forskolin to concentrations 10 and 20 micro moles.
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And then you see that there was an increased swelling in the isogenic IPSC derived controls, and this could be easily extrapolated and analysed subsequently.
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So that was the story about this the example about the cystic fibrosis model.
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And then we also try to see whether we could also use targeted approach.
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So this is the one typical experiment using ADCC based assay.
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So here on the left side, we have HER2 negative MCF cell line and then on the right side, we have HER2 positive BTB breast cancer cell line.
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So as you see like when these spheroids are cultured and exposed to activated NK cells, so there is nothing happening here the what we would expect.
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But when we activate these HER2 positive cells and then come with the Trastuzumab treatment.
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So you see there is already decrease in the spheroid.
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So and this can also be assessed using the segmentation.
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So we also see that the NK cells themselves are also causing some effect, but that's more like a scientific question which we would expect to certain extent.
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So this is something also we can say that when we do the labelling.
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So we can really actually get the change in the spheroids post treatment.
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So what are the applications?
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So first one I would discuss about the organoid.
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So you can do a label free analysis, you know, follow these organoids over the days and also see what's happening with the treatment post treatment.
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And then we also could do like single steroid drug efficacy based assays.
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This is a real sample.
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One of in my previous lab we used to work a lot on the patient drug models.
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So we acquired this ovarian carcinoma biopsy.
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So that just integrated the sample and did the spheroid based assay.
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So here we use cisplatin and docetaxel in combination.
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So in reality we would have had paclitaxel but due to lack of the clinical formulation we tested docetaxel.
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But the whole idea was whether we could also assess the fact of combination post treatment.
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And then we also can do multi parametric 2D based assays, whether it's an apoptosis looking at early apoptosis, late apoptosis or any other signalling assays.
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It's also many of our collaborators and research groups, they are using this specific imaging technology for toxicology assays where they need to have spheroids like which are even like, you know, using 96 well format.
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They need to have like uniforms, size spheroid that they can carry on toxicological experiments.
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And we can also do monoclonality based assays.
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So groups or biotechs, companies that are developing antibodies, so they need to really, you know, follow the single cell over the several days or week to be able to select that the clone like the colony, like for example, going from day one all the way to day 4.
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So once they have enough confluency then they can do further expansion of these cultures.
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And one thing that I haven't touched upon in the presentation was also that the new right outgrowth assays so many groups involved in the neurobiology programmes.
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So they need to study the neurotoxic effect of the drugs or any other or look for the compounds which are anti apoptotic or so they can also.
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So it's also possible to do these assays using to grow to follow these cultures over the time they form neurospheres and then all again form the 2D these neurites here like this.
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And then you can do multi parametric assays.
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You can stain up to three fluorescent markers and also utilise deep learning based approach here.
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So you can basically label the quantify these neurite outgrowths or the effects on the outgrowth.
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And another example that I already showed up is like if we were to do more target based drug efficacy studies.
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So that's also possible.
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So this is just in a nutshell what I just spoke about, and we'll be happy to discuss more if there is an interest or any other any more questions.
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So we'll be happy to discuss about those.
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We do our have our booth.
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So please stop by when you have time.
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And so now I'll just spend a few minutes on OCT Technologies.
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So here just an infrared laser based platform.
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So in this case here I'm showing you an example.
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We've been working with Ectica technologies quite extensively to see, you know, how we can what type of systems we can use from like a standard SBS formats or already hydrogel based plates that Ectica provides.
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So this is an example about using system organoid.
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You can see that it really takes cross section images and you can go through the core of the spheroid as you see.
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So here this spheroid is just what it is.
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And then you can really go through image through this core the other part.
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So this again like the, how the plate looks like and the same the video I showed in the past, the previous slide.
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And the other asset that we were also, we've been also working on developing is the glial stroma model, invasion model.
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So you see here we dumped the cells in the well in the plates and then followed this from six hours to 72 hours.
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And you see what happened like as you see like 24, 48, 72 hours, these cells are already started to migrate and which can further be quantified and in the next one, sorry.
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So this is again just quickly to show you like we had these endothelial cells which were followed over the days from day one to day 7.
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And then you see like when you look at the OCT derived images.
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So you go from day one and see how these vasculature formations are occurring over the day that you can really quantify venture subsequently.
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So this is for published data.
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So we're the group they developed the angiogenesis co-culture model.
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So using parasite endothelial cells in the PDMS based device pump does the cultures with VEGF and then we're able to monitor the formations proud formation here as you see.
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And then in this paper, they basically quantified is using imaging because they wanted to see or quantify this sprout formations.
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So this is just the application, the quick summary, what all can be done with this technology and so I stop here.
