What Cytometry Can Do for You: The Pros and Cons of Image and Flow Cytometry Webinar 31 October 2012

Transcription

1 What Cytometry Can Do for You: The Pros and Cons of Image and Flow Cytometry Webinar 31 October 2012 [0:00:10] Slide 1 Hello everyone and welcome to this Science/AAAS webinar. I m Sean Sanders, editor for custom publishing at Science. Today s webinar will be about all things cytometric. Flow cytometry has been used for decades as the method of choice to gather valuable data on an individual cell level. More recently, image cytometry technologies including high content analysis have emerged to enable more detailed cellular and subcellular analysis, particularly of adherent cell types. Image cytometry has become less expensive and more accessible, giving experts and non experts alike fast and easy access to detailed data about their cells. In this webinar, our esteemed speakers will share their views on the pros and cons of image and flow cytometry and how these technologies might be applied to their own research. It s my pleasure to introduce my studio guests for today. They are Dr. Bill Telford from the NIH in Bethesda, Maryland, Dr. Paul Gokhale from the University of Sheffield in the United Kingdom, and Dr. Liz Roquemore from GE Healthcare based in Cardiff in the UK. A very warm welcome to you all. Thanks for being here. Dr. Bill Telford: Dr. Liz Roquemore: Dr. Paul Gokhale: Thank you, Sean. Thank you. Pleasure to be here. You will notice that Dr. Gokhale is not actually with us in the studio today. Due to the big storm on the east coast of the US, his flight over was unfortunately cancelled, but we are very pleased that he could join us through an audio linkup. So welcome to you, Dr. Gokhale. It s good to be here, Sean. Thank you. 1

2 Before we get started, I have some important information for our audience. Note that you can resize or hide any of the windows in your viewing console. The widgets at the bottom of the console control what you see. Click on these to see the speaker bios additional information about technologies related to today's discussion, or to download a PDF of the slides. Each of our speakers will talk briefly about their work. After which we will have a Q&A session during which our guests will address the questions submitted by our live online viewers. So if you're joining us live, start thinking about some questions right now and submit them at any time by typing them into the box on the bottom left of your viewing console and clicking the submit button. If you can't see this box, just click the red Q&A widget at the bottom of the screen. Please remember to keep your questions short and concise. This will give them the best chance of being put to our panel. You can also log in to your Facebook, Twitter, or LinkedIn accounts during the webinar to post updates or send tweets about the event, just click the relevant widgets at the bottom of the screen. For tweets, you can add the hash tag, #sciencewebinar. Finally, thank you to GE Healthcare for sponsoring today's webinar. Slide 2 Dr. Bill Telford: Slide 3 Now, I'd like to introduce our first speaker, Dr. Bill Telford. Dr. Telford is currently the director of the flow cytometry core laboratory in the NCI Experimental Transplantation and Immunology Branch at the NIH. His main research interests include instrument development, particularly in the area of novel solid state laser integration into flow cytometers, flow cytometric stem cell detection and characterization, and functional characterization of early apoptosis by flow and image cytometry. Welcome, Dr. Telford. Thank you, Sean, for having me here today. Okay. So what I d like to do in the next few minutes is give a brief overview of just a review of flow cytometry for those who need it and also to talk a little bit about image cytometry, which is proven to be an important adjunct technique to flow cytometry. Slide 4 2

3 I think many people in our audience are aware of what goes into a traditional flow cytometer. You need first a biological sample, usually labeled with some sort of fluorescent marker. You need to move these cells in a linear stream through a focused light source, usually a laser, it doesn t have to be but it usually is. You then need to detect the fluorescence in a PMT with a filter and then you need to process that data out the other side. Slide 5 [00:05:01] Slide 6 Slide 7 Slide 8 So this is what the data ends up looking like and when we do flow cytometry, we re generally analyzing three major applications and I ll go through them one at a time. The first is fluorescent probes for cell surface or intracellular labeling usually using an antibody that s tagged with a fluorescent marker like fluorescein or PE. Here, we ve labeled mouse T cells for fast expression and CD8 expression using two fluorescent probes, FITC and PE. What I want to point out and I think this is obvious for many people who are familiar with flow but remember that all of this data is inter relational so that we can look at the cells that are CD8 positive in this star plot over here on the right and look at the Fas expression within those cells and also the Fas expression in the CD8 negative cells as well. Now this technology has become quite powerful. We can do 14, 26 parameter flow cytometry and the inter relational nature of it gives us tremendous power. Here, we re looking at lymphoctyes and monocytes and macrophages based on their forward and side scatter and we re able to look at the expression of numerous markers based on the ability to gate on one parameter and view another. All parameters are inter relational relative to the other parameters. Fluorescent proteins is probably the second most popular application for flow cytometry now. This started with GFP back in the mid 90s. There are now literally hundreds of fluorescent proteins that span the visible spectrum and this is another major application for flow cytometric analysis. Finally, we have a large variety of physiological fluorescent probes that can measure the physiological state of a cell. We can look at the mitochondrial membrane potential, the plasma membrane potential. We can visualize mitochondria, look at DNA content. The 3

4 number of probes available of this type literally are in the hundreds right now and another common application for flow cytometry. Slide 9 However, traditional flow has its limits. We re basically analyzing cells that are flying through a laser beam at about 10 meters/second and all we re generally able to do in that format is measure the total fluorescence of the cell as it passes through. We re not getting any subcellular data. We re able to look at the area, the width of the pulse, the height of the pulse here as the cell goes through. But generally the information we re getting per cell is very, very limited. It s just total fluorescence. This is where image cytometry comes in. Image cytometry has allowed us to actually visualize the cell and collect cytometric data at the same time. So we re collecting the same data that we would with a flow cytometry, but we re also able to get a picture, an image of the cell and various aspects of that image can become parameters just like fluorescence or forward scatter is a parameter. Another key characteristic of image cytometry is that the image and the cytometric data are correlated with each other. So you can look at a dot plot in a histogram and go back and see what that cell actually is. Slide 10 Slide 11 So this is what a typical image cytometer looks like. This is a Compycyte icys instrument. You can see a lot of the components are pretty much exactly the same as you d find on a flow cytometer. You ve got lasers, you ve got mirrors, dichroics, and filters. You ve got photomultiplier tubes for detecting that data. But rather than the data coming from cells in a stream, it s now coming from cells sitting on a slide or on a horizontal surface. The cells are static, they re not moving. Now we often refer to this type of analysis as high content analysis because we re getting more than just raw fluorescent data here. We re actually getting data pertaining to the interior structure of the cell. Another key aspect of image cytometry is that like flow cytometry it is event thresholded or event based. On a flow cytometer, a cell will go flying for the beam. You ll use a characteristic like forward scatter to determine whether an object is there or not and then you ll take fluorescence and other measurements off that cell. We do exactly 4

5 the same thing in image cytometry. We set a parameter, it can be scatter, it can be fluorescence to identify whether cells are present. The instrument will then segment or select that object as it has in these scans here and then the cytometric data is taken. So we have both image data, cytometric data and the two are correlated with one another. Slide 12 It can be really quite powerful and the imagery can be quite dramatic. These are mouse lymphoma cells that have been induced to undergo apoptosis using a drug, using camptothecin. We ve labeled them with three apoptotic markers, a caspase substrate labeled with fluorescein, Annexin V labeled with a red dye, Alexa Fluor 647 plus 7 AAD. We re able to get the image data and we re able to see the presence of Annexin V labeling or caspase activation in the cells. These images here are not photographs. They are actual PMT reconstructed data and this PMT reconstructed data, this image data is the data by which the cytometric data is then derived. You can see the cytometric data on the right hand side of the slide looking very much like you would see off of a traditional flow cytometry but you ve got the image data as well. Slid 13 [00:10:24] Slide 14 Now you can do a lot of very powerful things with this. We can look at the subcellular structure of a cell and express that structure as a fluorescent parameter. Here, we ve taken U2OS cells. This is a human osteosarcoma line that s been transfected with a GFP LC3 fusion protein. The LC3 protein during autophagy segregates into these very tight, punctate regions. It s normally distributed evenly throughout the cell. We can measure those punctate regions using image cytometry and express them as a parameter. So we re not just taking total fluorescence here, we re looking at the fluorescence structure of something inside the cell. Now there are a number of good image cytometer systems out there. This is a sampling of them. I ve shown you some data on the Compycyte icys. The BD Biosciences Pathway systems, the 450 and the 850 work very well in this regard. The GE IN Cell platforms, the 2000 and the 6000, the Cellomics and PerkinElmer systems as well. These are generally very sophisticated systems. They can collect many fluorescent parameters. They often have multiple excitation 5

6 lines either lasers or lamps. They archive the data and the data can be brought up later and correlated with the cytometric data. Slide 15 Slide 16 There is one stream based image cytometer system available. It s the Amnis ImageStream. Rather than capturing images with cells that are sitting on a slide or on a flat surface, this instrument actually captures the images in the stream. Here we have I believe these Raji cells that have been again labeled with multiple apoptotic markers, caspase, Annexin V and DNA and you can see brightfield and also the individual fluorescent data and this data can also be expressed cytometrically as dot plots and histograms. Finally, there are a number of special purpose image cytometers out there currently. The GE Life Sciences Cytell is one of these, the TTL Labtech Accumen, the Cytellect Celigo, the Beckman Coulter Vi cell. These instruments generally do not archive the data, but they use the data, the image data as the basis for the cytometric data they display. These systems are often designed for specialty or specific applications like viability apoptosis or basic immunolabeling. The imagery is generally not archived but sometimes intracellular features can still be quantified and these systems also distinguish themselves in generally being of lower cost than the more sophisticated systems. So they re very good for specialty applications. They can also have very rapid throughput for applications where you need to run large numbers of samples very quickly and Dr. Gokhale will talk about that in a few minutes. So I will conclude my talk at that point. Slide 17 Dr. Bill Telford: Great. And give it back to Sean. Thank you so much, Dr. Telford. Our second speaker for today is Dr. Paul Gokhale. Dr. Gokhale is currently a research associate at the University of Sheffield. Over the past seven years, he has been involved in the international stem cell initiative first to develop standards to characterize human embryonic stem cells and second to identify common genetic changes that occur when human ES cells are cultured in vitro. More recently, Dr. Gokhale has been involved in projects to use real time imaging and image analysis to understand 6

7 how human embryonic stem cells self renew and how early differentiation decisions are controlled. Welcome, Dr. Gokhale. Slide 18 Dr. Paul Gokhale: Slide 19 Slide 20 [00:15:04] Okay. Thank you very much, Sean. So in my short presentation, I m going to take you through a brief overview of high content analysis, briefly talk about why it fits in with other cytological techniques that people are familiar with, some of the types of assays we commonly use with these systems, some factors to think about on the pros and cons compared with flow cytometry. Then I ll go through examples of how we do the segmentation analysis so you can see how all this fits together. Okay. So where does high content analysis fit in the scheme of the sort of cell biological techniques? Well it fits really in between flow cytometry on the one hand which has very, very good throughputs and stats for generating signal cell data and the data analysis is very well refined over decades now. At the other end of the spectrum is the conventional microscopy, which is quite labor intensive, you get fantastic data, but it s not very good for looking at large numbers of cells and large numbers of conditions. So what assay do we really use high content for? Well there s quite a range now and it gets bigger all the time. So we ve got things like morphology so we can stain the samples for morphology for example with phalloidin to look at the actin cytoskeleton. We can look for things like blebbing. People look at neurite outgrowth and how neurons are branching, differentiation because of the cells change shape when they differentiate typically. The fluorescent intensity type measures, which is probably the one you would guess we would use it for which things like cell cycle, for marker expression, which is very similar to the flow cytometric type assays. Then we ve got things like these fluorescence distribution sorts of assays and this is where we look for translocation of proteins from the membrane to the cytosol or the cytosol into the nucleus. We can look at DNA damage by looking for foci in a nuclei and we can also look for co localization. So if we can label two sets of structures within the cell fluorescently, we can see how they co localize. Then we ve got the next sort of set of two which perhaps ones which are becoming more common now. The first one is cell behavior over 7

8 time. So this is now becoming more and more important particularly in stem cell biology is to actually look at cells using time lapse type systems and also high content imaging systems can do this now. It s used to actually get real time measurements of things like the cell division times, the lineage tracing, whether the cells are migrating, and again shape changes based on the imaging. Then finally another aspect which is becoming more and more common now is to look at what we term higher order structures and this is where we combine various features that we ve imaged together to create larger objects that you might be imaging. So the simplest one is that we create virtual cells within the software. So we tell the software that we need a nucleus and we tell we re going to use a cytosol and we tell the software that those tissue be linked together and then we can ask questions about the relationship between fluorescence. Within those two compartments if we combine lots of cells together particularly with this is pretty relevant for pluripotent stem cells, you get colonies and you can ask about what the marker expression is across the whole colony. This has now been extended to embryos such as zebrafish embryos and things like that and tissue distribution by looking at slides as tissue stained slides. Slide 21 So what factors do you need to think about when you re doing these assays? Remember, these are imaging based assays so they go into a microscope. So one of the problems that we encounter of course is that typically people will grow cells in tissue culture plates for example and these can vary greatly in their ability to be imaged accurately and easily. So 6 well plates can really very, very thick plastic actually compared with coverslips for example and so you may find you have problems with sensitivity in using those sorts of plates so you have to change to either much thinner base plates or change completely to using coverslips. The resolution required, you need to think about. As you use higher and higher NA lenses, they become more and more difficult to use with standard tissue culture plastic just because of the thickness and because of the way the plastic is made. It s not very even and things like that. So again, you may need to think about slides. The well size, this is one that most people don t really think about but can you really see enough of what you need to see in one well? You know, if the well size is small and you need to look at lots and 8

9 lots of cells, you may not have enough cells in one particular well. In the case of human ES cells, colonies, it becomes a problem that we need to get enough colonies in our well to be able to see something meaningful. Then the caveat of that, of course the counter thing to that is of course remember that, you know, different size magnifications it will take you longer to cover a particular area of a well or particularly a number of wells so that can slow the assay down and that maybe a factor for you. Then the final thing you have to consider is of course the number of colors. Remember that typically we can only deal with four colors, which is in contrast with flow cytometry, which can deal with more and also for certain fluorescent reporters and things they may be very, very weak and you may have to use specialized filter sets, which you do need to think about before you start these experiments. Slide 22 So as I mentioned, there are some pros and cons regarding when you compare with flow cytometry. So in the acquisition side, the first really big, first major difference you ll notice is the throughput. The flow cytometers are very, very good at getting single cell data from large numbers of cells. The high content analysis type systems are much better for large numbers of conditions with a sort of moderate number of cells. So that s the sort of thing you need to think about there. As I ve mentioned, the parameters, the flow cytometers have a huge color part they can see. It s typically up to 12, it can go even higher depending on the machine configuration. For microscope systems it s typical just four. The sensitivity again as mentioned before, the flow cytometers are very, very sensitive instruments because of the way the optics are arranged and the type of detection they use. The high content analysis systems tend to be less sensitive in part because it s going through a lot of glass in the objectives, in part because of the plates people use and the fact they re using CCD detection. Having said that, the machines are getting more and more sensitive due to better camera technology and better light technologies. So in theory, they should become more sensitive but you do have to watch that. 9

10 [00:20:17] Obviously, subcellular imaging then apart from the imaging flow cytometers, flow cytometers don t let you do subcellular analysis whereas high content analysis that s really routine for this type of analysis. Okay. On the data analysis side, the flow cytometers again with single data, it collects single cell data very, very easily and this is for all systems that you will use it will be very, very easy to get this sort of single cell data. With the high content analysis because the cells are stuck down and next to each other, you have to do image analysis to separate them into single cells first of all. The major problem there is of course some cells will pile up on each other where you may seeded them too densely and then that causes quite a lot of problems when we start to do the actual analysis. Another factor which again as I mentioned is becoming more fashionable to do is time series or time lapse type things and of course on flow cytometry, you can only really do that by sampling but on high content analysis systems now that s becoming more routine. The systems can be supplied with CO2, humidified CO2 systems and heated chambers and the software will enable you to screen the time lapse images together to get that sort of data really quite easily now. Of course, the final thing, which is again the major thing about high content analysis, is the cell cell relations are lost with flow cytometry. So in high content analysis, we preserve all that. We know exactly where we got the information from so those relationships can be reconstructed. Slide 23 Okay. So what s the actual process that we go through? So the process is summarized here. We acquire the image. We do what s called segmentation. Segmentation is where we pull out the features we re interested in from the image and this is by sophisticated image analysis algorithms. Then what we do is we then have to tell the software what the features are and how we wanted to handle it, which is called feature extraction. We then put some parameters into the software to tell it how we want to classify those features. The example that we got here, just the most simple example is you ve got some Hoechst stained nuclei from human ES cells so you can see these colonies in this picture. They ve been segmented. If we then do some methodical treatment to that segmentation, we can 10

11 then dilate where we think the nuclei are and of course that will show us where the colonies are because it all merge together. Then what we do is we just tell the software well the colonies are the big blobs, everything else as you go smaller is not the colonies. That s what you see in the final picture there where the colonies have been labeled in green and the rest of the cells have been labeled in red and that s how we then would extract where the colonies are from these images. Slide 24 So how do we do that extraction, that segmentation extraction or at least what does it look like? So we ve got some examples in the next few slides, which will just illustrate to you the types of segmentation you can do. So on the left here, we ve got nuclei segmentation and what will typically happen here is we will apply the segmentation algorithm and then we have to try and separate the nuclei using other algorithms to separate them out from each of the it s called clump breaking or anything like this. So that s because of they re very close to each other in some situations. On the right hand side, you ve got mitochondrial segmentation, which is of course they re really quite tiny features. For this, you would have to use high NA lenses. You need a nice dye for this and then there are algorithms, which can handle these types of small vesicular features. They are different from the ones which do the nuclei segmentation, but most software will have various algorithms which can be applied to different features. Slide 25 Slide 26 We ve got for the whole cell sort of the cytosolic level. This is just very, very simply you can label the cytosol with various dyes as Dr. Telford alluded to there or with fluorescent proteins and it becomes relatively simple then to segment the cytosol, tell the software that s the cytosol and away you go. Then finally sort of larger scale features again as I mentioned is the colonies and things like this, which is caused when we merge the nuclei that we segmented from the original Hoechst stain picture and this gives us where the colonies are. The red dots in this or the red features in this picture here in fact are fibroblasts which we use as feeder the cells, which are quite easily separable from the colonies. Okay. So what s the sorts of measures we can do? Well it s really anything you can think of that you could measure from an image 11

12 once you ve extracted the feature from it. So we get all sorts of things from images once we started this process going. Again, in this example of human ES cell colonies, which have been stained with Hoechst, have been stained with pluripotency marker with SSEA 3, we can get the number of cells in the colony, the number of cells positive for the SSEA3, how far apart the nuclei are., and then going up to the colony level how many colonies are in the well, what the shape is, what the area is, and also the spatial relationship between the colonies and things like the number of positive cells in the colonies. So you can really get a lot, vast amounts of information about what this culture is really like and remember all of this is the spatial information in particular was lost with flow cytometry so we tend to use these things in conjunction with each other. [00:25:34] Slide 27 This is an example of what the data would come out like eventually. So we ve done exactly the same thing here but we ve got an additional marker which is OCT4 another pluripotency marker. As you can see by going through all this analysis process, you can get flow cytometric like data, i.e., population histograms after these images either as univariate plots or bivariate plots. You can simulate setting gates just like you would in a flow cytometer and you can get numbers that are proportionate to each fraction that you ve identified. As Dr. Telford alluded to the advantage is we can go back to look at the individual cells in these images because the software records where it s made all these images, where it s made all this analyses from. Slide 28 Slide 29 This is another example just to show you how you can get this flow cytometry like data. This is stained for BrDU and the DNA stain and you get this sort of classical horseshoe plot to show the cell cycle phases, which is similar to what we would see with a flow cytometer. So it s really quite remarkable that you can actually get this out of really quite messy and complex images. So I ll finish there and it just remains for me thank various people in various labs, which help us do all this sort of stuff because we actually make reports and all sorts of things to actually test these 12

13 systems out so it s actually quite a complex exercise and I ll stop there. Slide 30 Wonderful. Thank you so much, Dr. Gokhale, and thank you very much for joining us even though you couldn t make it into the studio. It s great to have you on the line. Our final speaker for this webinar is Dr. Liz Roquemore. Dr. Roquemore s training is in diverse aspects of intracellular protein regulation, membrane protein trafficking, and cell phenotypes. She is now the technology manager for cell applications within the Cell Technologies Division of GE Healthcare Life Sciences and has played a leading role in the development and validation of cell lines, reagents, assays, and imaging tools for research and drug discovery. Her major focus area for her work has been the application of high throughput microimaging and analysis technologies for cell biology research and drug development, most recently investigating the combined use of stem cell derived model systems and high content analysis techniques to enable earlier predictive toxicity testing. Welcome, Dr. Roquemore. Slide 31 Slide 32 Slide 33 Thank you very, a pleasure to be here. So after that great overview of HCA, I d like to take a step back and just give you sort of an overview of image cytometry and the range of instrumentation that s available for doing image cytometry and also the types of applications that those platforms are best suited to. So image cytometry in its broadest sense can be defined as the process of extracting and making sense of cytometric data obtained from images of cells. So as alluded to in the previous talks, those images can be obtained in a variety of ways with a variety of systems, but the emphasis is not so much on the images themselves in image cytometry but on the data that we can extract from those cells and the knowledge that we can gain from that process. Image cytometry has four basic components. Those include the cellular samples of course that we re working with, the probes and sensors that we add to the cells to understand their function phenotype, the imaging platforms that we re working with, and not least of all the image analysis software which allows us to turn those 13

14 images into data. All four of those components are continually evolving together so that advances in any one of those areas propels advances in the others. So we re seeing a lot of very exciting developments now in each of these four areas as image cytometry progresses. Slide 34 So why imaging as an approach to cytometry? Well I think the two previous speakers have summarized very nicely the fact that with image cytometry, we can gain quite a lot of information about morphology, structure, kinetics and position and location of cells and also the orientation of cells and groups of cells that we may be studying. Also as a lot of the higher content imaging systems can process many samples in parallel, many samples very quickly, we can do higher throughput studies, larger scale studies all the while collecting very comprehensive data from the cell population and building up unique fingerprints as to the cell phenotype. [00:30:13] Slide 35 Slide 36 Slide 37 Now we re also seeing the emergence of more convenient image cytometry systems, which are more compact, more affordable, more accessible to the members of the laboratory and they can be located throughout the lab, and you don t necessarily need an expert to run those systems or any extensive training. So image cytometers really fall on a spectrum from those which focus on depth and breadth of information that we can gain and the others that are specialized more for ease of use and accessibility. So let s take an example form the depth and breadth end of that spectrum and we ll look at cell cycle analysis as an example. So perhaps one of the simplest ways of looking at cell cycle phase is to do DNA content analysis and when we re working with adherent cells, it really makes a lot of sense to go for an image based cytometry to do that because we don t need to disturb the cells to be able to quantify the DNA content. Doing that, we can get results, which are strikingly similar to the results that we would get with flow cytometry. So here for example, 14

15 you can see that we re getting very similar cell cycle phase profiles but there are two main differences. The first is that because we re working with an imaging technology, we can process the samples very quickly and also because we re not having to remove the cells from the dish or undergo any centrifugation wash steps, the whole process is taking us a lot less time on a high content imager for these adherent cell types. The second big difference is that not only do we get these cell cycle phase distributions from the intensity information, but we have a lot of morphological information that we get virtually for free with no extra time or effort. Slide 38 Slide 39 Slide 40 So here you can see for example that when we treat with mitomycin C, we re getting a dramatic increase in the nuclear size. We can follow that for every cell in every image and that can give us more confidence in the conclusions that we draw from our cell cycle phase analysis, but it also can lead to new insights into the function and the mechanism of action of our drugs. We can add a second sensor as shown in the previous talk to further delineate the S phase cells. In this case, we re looking again at incorporation of bromodeoxyuridine and we re getting that classic horseshoe shaped distribution of the cells. But things get really interesting if we get a third sensor to the mix. Here, this is something that we could only do with an image based platform because this is a GFP sensor, which marks the cell cycle phase position of the cell by not only its intensity but the position of this probe within the cell. So now, we see something very interesting here. So we ve again got that classic horseshoe shaped distribution of our control cells in blue and our cells treated with a test compound in red. You can see that the test compound is causing that horseshoe shaped plot to shift to the right, but now we ve got a third dimension added to this plot and that s represented by the diameter of the spheres that are plotted here. Those diameter of the spheres are telling us about the cell cycle phase position as reported by this GFP reporter. What we find is that even though the cells on the right which are 8n are in the position that you d expect G2 cells to be, they actually 15

16 have characteristics of G1 phase cells. So by using this GFP reporter, we ve learned a little bit more about the mechanism of action of our compound, which happens to be where our kinase inhibitor and it s causing the cells to undergo a process called endoreduplication where the nuclei divide but the cells cannot undergo cytokinesis. Slide 41 So I hope that gives you a flavor of some of the many types of things you can do with an image cytometer that you maybe you couldn t do with other technologies. There are some emerging trends in the instruments that are available to do this and some of those include a more complete solution. So increasingly, we re seeing systems which take us all the way from acquisition through to analysis and even interpretation of our results seamlessly. We re also seeing more fluorescence based kits and assays available that can really cut down on our assay development times. Another thing that we re seeing is that the systems are all getting quite fast and they re able to collect much higher quality images than ever before. That s very important because the quality of the data that we get from HCA is only as good as the images we start with. A third trend that we re seeing is towards more flexibility with confocality. So instead of being locked in for example to a particular pinhole sized confocal aperture, we re now seeing systems which give us variable aperture and that can really give us more flexibility in terms of the applications that we can cover. All of these improvements are being made possible by new technologies being incorporated into the systems including scientific grade CMOS cameras, diode lasers and LED technology. [00:35:20] Slide 42 Slide 43 But what about the more routine assays that we do every day in the laboratory like counting cells and doing cell health and phenotype analysis? For those systems, maybe high content analysis is a little bit of an overkill but we still want to get some of the benefits of imaging. There, we might want to turn to systems, which are more focused on ease of use and accessibility. A good example there is cell count and viability. So this is an assay that we do every day in the lab. It s very simple and we really want 16

17 to be able to do it anywhere, anytime and not have to turn to an expert to do it. So typically we ll run a two color assay like this one where we re marking the total cell population with a red dye and the dead cell population with a green dye and in this case we were running it on a Cytell Image Cytometer and you can see that we re able to get very familiar results. We can set our thresholds to separate the live from the dead cells and we ve got a selection of gating tools so that we can do further subpopulation analysis. Slide 44 Then we can generate dose response curves from our data and here s an example of a camptothecin dose response curve and its effect on cell viability. In case you re wondering what that little disc is in the upper right hand corner, that is the sample holder for Cytell. It can hold eight different sample cartridges. So we d simply put a small for this dose response curve, we just put a small volume of sample into each of those eight cartridges and we get the results for the entire dose response curve in about two and a half minutes. Now these are the results with Jurkat cells, which are a suspension cell type. But what we find is that whether we re working with suspension cells or adherence cells, we re getting results which are strikingly comparable to other image cytometers. Slide 45 Then my last example is one where we were working with peripheral blood monocytes and we were developing an enrichment procedure for T cells and this is another one where an image cytometer came in handy. So here for example, you can see our results are showing us that our cells are about 96% CD3 positives. So this was a good verification for us that our T cell enrichment process was working well. The other thing that we wanted to know about was the ratio of CD4 helper cells to CD8 cytotoxic T cells. That s typically about 1.5 to 1 in the average individual and here you can see our results that we got with Cytell showed we had 1.5 ratio, which corresponded very well to published results. Another thing is that we wanted to migrate this assay from flow cytometry, which we do quite a lot of in the laboratory, to an image based cytometer so that we could handle this routine assay a little bit more readily within the lab. So we did a little bit of benchmarking and here you can see that we ve got strikingly comparable results between the Cytell Image Cytometer and the flow cytometer that we had in our laboratory. 17

18 Slide 46 So what are the things that we had to consider when we were thinking about doing these more routine assays on an image cytometer? Well of course, we thought about the level of expertise available in the lab and where we would like this instrument to be located and we re finding that some of these more value image cytometers can be located anywhere and don t require a lot of expertise. Another thing to consider is whether we go with predeveloped acquisition and analysis protocol or user defined ones. We like a mixture of both because the predeveloped ones can save us time and expertise but the freedom to create your own can give you some flexibility. Speaking of flexibility, it s also good to have flexibility for the various dyes so that we re not locked into any particular assays. If we re working with an open system like this, we need to think about the number of excitation and emission channels that are going to be available. Last but not least, speed is something that needs to be considered. So when we want to think about the speed all the way from the time we load the sample to the time we get the results, and if we can run multiple samples in parallel that can really save us time. Another thing to remember is about the hidden cost, time penalties that might be associated. So for example if a system needs to have a lot of setup time or if there s calibration involved or wash steps, those can be time penalties that maybe you hadn t counted upon. Slide 47 [00:40:03] So just to summarize, I think in our experience we found that image cytometry and flow cytometry are very complementary. One is not necessarily going to replace the other but with image cytometry, we can get quite a lot of additional morphological information. We can use adherent cells or suspension cells and increasingly the technology to do that is becoming easier and more accessible. Now we re in a state where we ve got quite a lot of instruments to choose from and so we can actually afford to match the image cytometry solution to our needs rather than being driven by the instrumentation in our assay choices, and that s really a smart way to go. 18

19 So for detailed phenotyping and functional studies and screens, we re finding HCA has quite a lot to offer in terms of the power and flexibility that we have. For the more routine assays, I think it s really worth looking at some of these value image cytometers that really can bring that capability right to your bench top without a lot of added expertise required. Slide 48 Slide 49 Slide 50 So finally, just to show you some of the image cytometry solutions available from GE healthcare that we ve used to generate the data in this presentation. At the bottom I ve got the IN Cell Analyzer 6000 which is our confocal version of the high content imager, it s got variable aperture technology, the IN Cell Analyzer 2000 which is our very flexible wide field system. Our most recent introduction is the Cytell Image Cytometer, which is quite compact. It s got two lasers and four emission channels and really does allow us to do quite a lot of the routine assays everywhere in the lab. So that brings me to the end of the talk and I just like to thank people for their attention and hand back over to Sean. Great. Thank you so much, Dr. Roquemore, and thank you to all of our speakers for the wonderful presentation. We re going to move right on to the questions submitted by our online viewers. Just a quick reminder to those watching us live that you can still submit your questions by typing them into the text box and clicking the submit button. If you don t see the box on your screen, click the red Q&A widget and it should appear. So we ve got a lot of questions so I m going to try knock through as many of them as possible. But the first question I m going to ask is about the percentage of assays that are flow versus HCA in your experience. So I know all of you have done both of these so maybe you can talk to that in your particular laboratory, Dr. Telford? Dr. Bill Telford: Sure. Well I run a core facility at the National Cancer Institute so we probably have about 80% of our work being flow cytometry and the other 20% being image cytometry. But the image cytometry tends to be done for very specific purposes where the imagery is really critical. We do a lot of apoptosis analysis on the image cytometer 19

20 because you want the morphological data along with the fluorescence data and it s very powerful for that purpose. We have some other interesting applications too. We do a lot of cellcell based assays where we want to look at cell cell interactions and that s something again where you re not going to get that information out of traditional flow in the stream. You ve got to have the cell sitting on a dish to collect that data. We have people who do very small numbers of cells where they ll just dab them on a plate and allow them to settle. We have applications where people want to analyze their GFP expression in a 96 well plate, but they don t want to discard the cells. They want to put them back in an incubator and keep running them. With these image cytometers, with many of them, you can put the plate with the lid on, on the instrument, scan it, and then put it back in the incubator. Again, you haven t destroyed your cells as part of the analysis. So a lot of specialty applications like that where flow cytometry really doesn t provide the advantages that image cytometry does. Dr. Liz Roquemore: Dr. Paul Gokhale: Great. Dr. Roquemore? Well I think our experience is very similar to yours in that we probably have about 30% of the people in the lab doing flow cytometry and then maybe 70% of the assays would be high content analysis. We do a lot of cell production and I think flow cytometry is very useful there. But increasingly we re finding these bench top image cytometers can really get the job done quickly. We don t need to have a lot of trained experts to do it and so we re getting those being used more and more often. Then on the high content analysis side, we re finding that we can get really detailed cellular phenotype data from the HCA, which we just couldn t get by any other means and that s really breaking new ground in terms of understanding how compounds act on a cell and predicting their toxicology, toxicity for example. Great. Dr. Gokhale? Yeah it s very similar actually to Dr. Telford. It s about 80%, 20% and it s for very similar reasons. It s where we need morphological information, we will do the in situ high content assay or for time lapse imaging. Uh hum. Excellent. 20