Okay, so yeah, module seven, we're gonna be talking about single cell RNA sequencing, I've been following some of the discussion on the slack about the single cell DNA as well. So some of those lessons are also going to come into play here. So if anything is unclear, especially this bridge between the world of DNA and RNA, this is the this upfront sections probably the best place to cover that all off. So all creative commons, of course, so Francis is really highlighted how important this part is everything here is either published or on a website somewhere. So certainly share and share like these slides are free for you to use. Hi, so much all seven. So my name is Trevor Pew. I'm a senior scientist at Princess Margaret and I'm the director of Genomics at OICR, and I'm sharing this time with have your DS and have me I'll let you introduce yourself. Thank you Trevor, I will open my video in a minute. But yeah, my name is Javier Diaz. I'm Trevor as a scientific associate for about three years at UHN Princess Margaret Cancer Center and starting in March this year. I joined a startup called Phenomic AI. We do cancer analysis using machine learning and artificial intelligence and I'm the head of data science there. And also highlight have your also is the organ answer about the Toronto single cell, our name single cell aren't single cell working group, which I'll plug at the end. I think some of you are already members, but happy to continue interactions with this group through that that working group as well. The goals for this afternoon are to know the difference between Balkan single cell sequencing seems obvious but there are a lot of little technical caveats, especially in the upfront pre sequencing step that are really quite fundamentally different. I'm going to talk very largely about how is single cell sequencing is being applied specifically to cancer samples and how it's different from how work in on cancer cells is done. The caveats just like an RNA sequencing, again, following the slack conversations there's just a lot of nuance around how do you generate your data, all those caveats apply here to single cell as well. And probably the most exciting part of this is really a hands on work with real published data, and most of the figures towards the end of my talk, have you will essentially teach you how to make on your own. So first two pieces. What are the technologies, and then we're I'm going to use the Gleoblastoma stem cell data set that we recently published as an example of all the things that you can pull out and derive from single cell RNA sequencing data. And then picking up that exact same data set, have you will take you through what a single cell analysis would look like right from reads all the way through to to data sharing. Okay, so we're going to talk about technologies to start. There are so many now cliched ways to communicate single cell sequencing and how it compares the bulk this is my version. Really thinking of tumors I'm going to be careful with the language here, the tumors being a collection of all cells that make up a mass an oncogenic mass, and those tumors contain a diversity of cancer cells immune cells and other cell types. I'm going to talk about the bulk myeloma bone marrow up next to a bowl of M&Ms because it conceptually is kind of the same. What defines a blue M&M what defines cancer cell, what defines infiltrating vascular vessel or T cell. And the challenges are really the same if you're really interested only in blue M&Ms or blue smarties how do you go through and pick out those cells specifically. We really couldn't do that with bulk sequencing the old approach would just be grab a handful of M&Ms and eat them or analyze them. Now we don't have to do that you can literally pick through the bowl and ask different questions of different types of cells. And this is the formal way to show that so single cell transcriptomes really let you see heterogeneity that you can't couldn't have seen using bulk sequencing. So the bulk approach is essentially take these six cells you grind them up and you get an average. So there's six green dots you get a square of six. Now we get a different gene expression level for literally every single cell, but there are a lot of technical caveats with with that increased resolution. So specifically our ability to fully read out all genes being expressed by a single cell with that specific topic will go into a little more detail. But before we get there I really wanted to go through the generic approach to single cell sequencing. If you try to keep up with a single cell literature you'll rapidly become overwhelmed by the huge number of ways to process a sample. So digest the tissue do you use full cytometry. Are you working with a mouse. There are lots of ways to get single cells floating in a tube. I'm not going to belabor that point but there are really lots of ways to get to a single cell suspension. So there the goal is to really individually barcode each of your pieces of RNA for each cell and there are lots of ways to do that we're going to be talking a lot about the 10 x genomics platform. But certainly there have been very manual ways using flow cytometry putting each cell and individual well 10 x genomics you basically pull each cell into an oil droplet, the ways to do this with antibodies now. This military barcoding technique probably doesn't have as many approaches as tissue digestion but there are still lots of ways to barcode a RNA and assign it to a cell, and probably more generic at the end is sequencing so this is still the common currency. And really has a dominant position in the sequencing market. So a lot of the data will actually come out certainly in terms of fast cues, really do look the same and there aren't really that many different approaches to sequence DNA align it. So there's a lot of sort of debate around how to normalize a cluster, and it's particularly challenging in in cancer samples and we'll go through that, that in more detail as well. But this in general is sort of a generic approach to a single cell sequencing experiment, and especially as bioinformatics out and that analysts, really understanding which is, I'd say is sort of down here in the third box. And it's extremely important to understand how the up the two upfront processes were done because it will really totally change how you approach single cell data. And this paper is basically a review of all the ways that you can isolate those singles cells. So the very early days of single cell sequencing it was literally a pipeter and single cell suspension. So we just pipette each cell one by one into a well and do what's essentially a bulk RNA sequencing reaction in a single well on single cell. Other approaches. FACTS is probably the other approach rather than using pipeter using a essentially a flow sorter to pull out and assign cells individual wells. So after my today section actually this a lot as a graduate student, literally a laser to cut around a cell of interest and you pull it out. More conventionally this is how the 10 extra notes platform really works essentially you digest your tissue, you digest your tissue into a single cell suspension, and then that is loaded into a micro fluid fluidic device. In this case, this works essentially because you can deliver your single cell suspension into an oil droplet that oil droplet has reagents you need so it's conceptually very similar to a plate. You can do this in much higher throughput. And there are extremely fancy ways to pull specifically panel after talking about looking at circulating tumor cells. So essentially using antibodies or other ways to capture cells to lift those cells out of suspension. So it's nice to say, this is probably the most. This step here is probably the step that will influence your data the most downstream, and it's very important to control a lot of the parameters around how exactly you're isolating and pulling out your single cells. Once you have your single cells. There's lots of ways to to generate the RNA sequencing libraries I put this title in quotes this is literally the title of the paper cited at the bottom of the slide. So like the DNA sequencing capacity you can really just see the huge increase in a number of cells that can now be profiled in a single experiment. And this is just continued to grow since this paper in 2018 and lots of different ways to get there. At the end of the day the concept is really the same. It's essentially an RNA profile of a single cell, profiled to varying levels of depth in terms of transcripts for cell, but also breadth in terms of coverage to pass a full RNA transcript. The two dominant technologies currently for generating single cell sequencing data are the 10 x genomics chromium system which is essentially profiling and reads of your RNA, either from the three prime and or the five prime and technology but still very very widely used smart seat to technology and I did want to go through these in a little bit more detail this paper goes into even more detail than this. But I did want to hammer home the effect of using these two different approaches on what your data will look like when it comes out of the other the sequencer. And the big difference here specifically is this poly a priming and actually both technologies will use this poly a prime so that's already caveat number one, if you're really interested in transcripts that don't get poly a primed circularization circular RNAs, don't go looking for them because they're very unlikely to be found if your transcript is not poly denulated. So the method you essentially prime off that a you it amplifies a relatively what was previously thought to be a relatively short distance is actually turns out to be quite a bit longer than previously thought. You then amplify those transcripts by PCR, and then there's this tagmentation approach which essentially randomly inserts into the transcript and results in a little tiny short tag here at the end. The big difference here smart seat to is you actually read to the end of the transcript and then there's this little step at the end called template switching so when you get the end of the transcript, the enzyme to start parking in seas and essentially add your, your barcode in at this, at this point. And basically it's the exact same idea this tagmentation approach delivers these sequencing adapters randomly throughout the entire transcript. And the huge benefit of the smart seat to is now you're actually getting full end to end reading of your of the entire transcript, whereas the 10 next genomics approach really is just giving you this little short tag sufficient for gene expression but not going to let you get any of the other effects on usage or potential splice isoforms fusion transcripts that you would get from what we're used to from both RNA sequencing. As a result, there's also a huge cost difference the smart seat to is extremely expensive because you have to pay to sequence across the whole transcript 10 X genomics you're just sequencing at the very end so your cost for cells much lower. You can almost tell just by reading the abstract which technology they used. So a lot of smart seat to papers are in the hundreds and 10 X genomics papers usually in the thousands now after 10 three even hundreds of thousands of cells being profiled. And this is really the effect on this data, even if they no one told you what the, what technology it was you could tell just by looking at the coverage of of reads across entire across the transcript. So here's a smart seat to data looks like so here's a generic transcript. This is a little three Exxon gene. And in smart seek you see great coverage across an Exxon, nothing in the intron great coverage across another Exxon nothing intron. It looks like bulk RNA sequencing data, but it's at the single cell level contrast that to the tax chromium, you get this huge spike right the three prime and and essentially no coverage. It turns out there aren't since you are poly a priming if there is a little stretch of a those little regions do get amplified as well so depending on your bioinformatics pipeline. You will only look at this region. So increasingly we're starting to use this off off poly a priming as an approach to start to call mutations deeper into the transcript because that data are there it's just whether your pipeline is actually utilizing that information. As I alluded to a little bit earlier, in general, you're just able to financially afford way more cells using 10 x chromium because you're just sequencing this little end. Whereas with the smart seat to approach, you basically have to pay for the sequencing reads that are mapping to all these dexons. So in general you're sequencing fewer cells, but you are getting a deeper profile so in this example here on average. On average you're getting sort of north of 7500 cells, whereas in the chromium approach, there is this issue of transcript dropout where you're actually seeing 4000 or even fewer cells being our genes being expressed per cell. Trevor question. Yep. So why do you see that plateauing effect when you're using the chromium. It's not 100% understood it is thought to be molecular dropout during the amplification within a droplet. So essentially you deliver the cell, you then lice the cell and all the barcodes get delivered to it. And then there is some loss here, due to this tagmentation effect because you're only capturing the transcripts that have to be sampled here. So it's essentially a, I don't call an artifact but it's like a function of how the RNA is prepared in the, in the, in the lab. I see. Yeah, they have been improving since the first version they are in version four of the kids now, I think the experimental kids, and they've been increasing the number of genes and number of reasons that can come from each individual cell. It's basically a physical limitation of how most reaction can happen within the droplet. And the amount of material that you have for the PCR there is not that you have to do the reaction you only have a small volume to do all the reactions, but they've been increasing it. And also a similar concept is also a challenge with the single cell DNA sequencing, because if you have two, two copies or two alleles, you lose one, you're never getting it back it's literally the only copy. So same idea in RNA, if you're expressing a gene at a very low level, and you just happen to not amplify it, it's gone for good, where the benefit of RNA is the highly expressed transcripts at least there's multiple copies so you're very likely to see them even if you have some attrition. So does that fully answer your question. Do you have any follow up to that. No, that answers it. Thank you. Thank you. Yeah, please break in with questions like that. That's actually a great point. And so we've sort of talked about single cell single cell isolation. We've talked about how to how the RNA is actually amplified and how data is made. And our slide really on bioinformatics tools in general. So this is actually reviewed relatively recently in this paper here. Really trying to address some of the issues that do come through in single cell RNA sequencing data. There's two major factors technical variation is by far the big one we spend a lot of time really as a field measuring quantifying our batch effects, different cells are captured to different efficiencies, even trickier depending on doing school cell or single nuclei sequencing. We're not going to talk too much about nuclei sequencing, but essentially you're freezing the sample the very will come out of freeze at different viabilities to actually get by us just from the act of freezing a cell amplification bias very similar to regular RNA sequencing sequence bias. And of course we have biological variation as well. So just like having technical replicates and RNA sequencing, you look at 1000 T cells are going to be some natural variation around that. I alluded to this also on the slack channel this concept of primary secondary and tertiary analysis, the same concept really holds here to single cell sequencing as well. So there's the generation of data which I largely just talked about that going through either smart seat to or 10 x genomics and into the sequencer. By far, the most important is q seeing your data. Do you have the expected transcripts a number of transcripts for cell, your cells classify as cells that you thought you're putting into the experiment. So the top of copper copper number variation the other copper number variants in what you suspect are the malignant cells. Do you have to throw out a sample you're just not getting enough reads or do you need additional reads to fully saturate a sample that really don't under and spend a lot of time on the secondary analysis it certainly saves you a lot of pain. So when it comes to what I find is kind of the fun part, which is that tertiary analysis, how do you derive biological meaning from a well normalized well formatted and structured data set and to me these are really three separate examples. And at least in my lab we've moved more towards this exact model when we talked to both our collaborators but also sequencing course. So we'll get you through primary they'll even get you to secondary analysis but we'll take it to tertiary analysis, do you need a full support, all the way through this entire process, or do you just want the reads and using phraseology like this, and linking it to specific paper just make sure that everyone understands who's doing what and where and when. And this is also the tertiary part is really where this computational creativity, really defining a strong biological question are absolutely the number one thing to do a single cell sequencing certainly in the early days was very tempting to download every package and attempt to run it. It's sort of fun from a technical perspective. But it really doesn't get you very far scientifically. So as we go into the, both the background to the glioblastoma project, but also into the workshop it's very important to know exactly at what's what question you're trying to answer at each step in the analysis and we have projects that are working in both of the, and all three of these spaces. What are the cells that reside in a tumor, how do they change over time in a purified cell population, how are those regulatory networks changing between two states are very interested in pre treatment post treatment. And how do cells develop both cancer cells but also immune cells to infiltrate tumors, but really building a specific question around each of these analysis, rather than the approach of just grinding through and running every algorithm that could possibly mention a single cell sequencing, especially when it comes to cancer genome analysis. And I certainly caution the group, a lot of tools, especially these trajectory tools are really being built and driven by developmental biologists, assuming two copies of every chromosome and well behaved regulatory networks. Absolutely not that they're aneuploid you have lots of different copies of the chromosome sometimes you're missing chromosomes, and all of these have effects on underlying distribution gene expression patterns. So really sort of treading carefully applying tools that are built for developmental biology, they will almost certainly give you an output. So in terms of biome from actual functioning, but it won't necessarily reflect the underlying biology of a cancer sample with an unusual karyotype. So I'll pause it there that's sort of the introduction to technologies are any questions at this point before we dive in specifically to the brain cancer example. Okay, so you have these concepts concepts can be a little abstract without a sort of a solid set of questions or a solid data set and that's really why heavy and I thought we focused specifically on recent publication specifically looking at the cancer stem cell populations that give rise to glioblastoma a highly malignant brain cancer. So the whole concept for the entire data set is really this idea that glioblastoma is a rise from cancer stem cells and it's this very rare minor population that actually give rise to the ball. So the whole point of generating this data set was to take bulk tumors, and the goal is to really find treatments that specifically target cancer stem cells and the model that the data set assumed going into the study was that current treatments would debulk the tumor but they leave behind these cancer stem cell populations, and it's these cancer stem cell populations that repopulate the bulk tumor and give rise to relapse. And the idea here is, let's isolate the cancer stem cells understand them and come up with therapies to treat them. This ideal stem cell cancer stem cell specific therapy will remove the cancer stem cells. As a result, you'll see regression of the tumor, even if you're not actively targeting the bulk right off the bat. So this data set is specifically a set of cultured glioblastoma stem cells they have a primary GBM just associate it just like you do for a single cell sequencing. In this case they culture it. The culture conditions select for brain tumor stem cells, and then these are then expanded into lasting cultures that can then be sequenced, and their entire research programs in this case Peter Durkin Sam Weiss that characterize these cancer stem cells using a variety of other functional assays. And this data set here is essentially looking at what transcriptional programs are active across a collection of glioblastoma stem cells. So the first question is relatively simple. Our brain tumor stem cells comprised of genetic and transcriptomic subpopulations. So are they homogeneous like cell lines were once thought to be we now know they're not. Or are they heterogeneous so they're actually multiple populations within the stem population. And is it possible to nominate a drug that is specific against one or multiple clones. So this is the first plot so the experiment here, the single cells have all been dissociated. In this case they've all gone through the 10 X genomics chromium platform. In sequence, they've gone through batch correction, some level of normalization, and have gone through the the long ranger, the long ranger pipeline. So have here we'll teach you how to go from the fast cues to plots like these. These are Disney plots which are analogous to principle component plots, going to a little more detail on this specifically in the workshop. And what I've colored here is essentially cluster assignments for one by going through samples one by one for distinct transcriptional populations and there's an algorithm here that essentially has a dial that lets you essentially resolve some of these populations from one another. So I ordered these basically by the number of clusters present in each in each GBM or believe us them a stem cell population at the top are the ones that are largely clonal so essentially one or two clusters whenever there's two clusters. The second cluster is almost inevitably an actively cycling cluster and we see these in patient samples as well. There's always this hyper proliferative malignant population, and they score for cell cycle markers and pathways like K67 DNA synthesis, these types of themes. And I've ordered these so essentially as you get later on the PowerPoint side you start to see additional clones until you get to this very biologically distinct IDH mutant. So this is really just the first trying to answer that first question are these highly heterogeneous, and the answer was, it depends in general there are not that many clones coming out specifically in this GSC population, but there are some very complex clonal structure present in some of these lines. Which is really drawing on what we knew from about Leo Westoma so in from bulk DNA and RNA sequence and we know that there are specific copy number alterations. You can also call out very large arm or chromosome level changes from RNA sequencing data. And the way this works is looking not just that individual genes but looking at whole cassettes of genes that exist on a chromosome arm or cross entire chromosome. And looking for that whole collection of genes to be over or under expressed versus a baseline. And the key to calling large scale copy number alterations from RNA sequencing data is to have a high quality reference. This is extremely hard to do from bulk RNA sequencing data because you have so many different cell types contributing to that bulk. In the cell sequencing data you can actually take in each individual cancer cancer cluster and normalize the gene expression values against their original cell of origin. So that was sort of the secret to getting this to work is doing a genome wide inferred copy number analysis from the RNA using normal oligo denture sites as the control and this is really the key to getting essentially the visualization of copy number variants we knew were there from RNA sequencing, but also be able to do a very deep dive on specific populations. I've just zoomed in on one of those Disney plots here. You can see there's copy number alterations that absolutely every cluster has so this game of seven is sort of a hallmark of glial Westoma, but you can see the sub population here marked in orange, essentially had this loss of chromosome 10 is not to be targeted by P 10 but you can see they're not in every single population so already you can see just by either subclonal structure, and you can play this game over and over so there's a sub sub clones this chromosome 21 gain or chromosome 20 gain, and that's completely unique only to this population. And then italics here the word partially, because these copy number profiles are not explained completely by copy number variation, even within this clonal population there are specific sub populations that are essentially not explained well purely by their underlying copy number profiles, and you can play the sub sub clonal game over and over, like is this little loss of chromosome six a very important and unique sub clone. It's not as briefly colored or called out as a sub clone, but this is really where the biological analysis and data exploration really comes in. Suffice to say it is possible to call a large sale of copy number variants from single cell data, not just at the ball population level but actually for each cluster individually, and have your will tell you how to do this in practice so you'll be able to make a plot like this later on this afternoon. Let's bring back out a little bit across all what we're looking for is a magic bullets that would allow us to understand all of the glioblastoma stem cells together as population. We did see that all patients live with some of them cells were different, but was there any sort of common biology that really brought them together. So this is the exact same concept this is just yet another in this case it's a map plot which serve an updated version of the Disney plot, and you can see each patient is really clustering completely distinct this is very has been reported many times many single cells sequencing experiments, the malignant cell population which these glioblastoma stem cells count are all highly unique they virtually never cluster together across different patients contrast that to the normal cells that we would see in a lot of these tumors that do intermix very strongly so for example if we took 50 primary brain tumors, the normal cells will all intermix across multiple patients, but invariably each cancer or malignant population will form its own distinct group very similar to what's shown on this plot here. You can see there is some relationship between two to spatially distinct tumors taken from the same patient. So whenever we had two lines from the same patient. These were closer to each other, or at least they're more transcription related to one another. But they didn't sort of beautifully intermix like a normal T cell or immune cell population from them necessarily would be analysis is the state of essentially uncovered a gradient. Glioblastoma stem cells that express more developmental populations are at the top. And the second principle component was is these injury response programs, essentially these lines down here at the bottom. And if we score out those two specific programs. But those two axes were mutually exclusive. So you basically scored out the developmental score and an injury response score, and they were largely independent of one another. So if we take these two axes, injury response and developmental program, essentially these lines are either heavily developmental, heavily injury response, or a mixture of these in between we never saw a line that really populated both ends of the axes simultaneously. These ones in the middle are kind of on their way on the gradient, but we never really saw one line that fully occupied both ends of the gradient. And this is really another way to look at that data, the exact same scores but just looking at the distribution of cells across an individual GSC. And the, the take home message from this was really the need for multiple samples to fully characterize these, the, the full spectrum of transcriptional states between this injury response, and this developmental program. But this is really the power of using single cell sequencing because you get multiple measurements of similar biology and you really get nice distributions of these scores across multiple samples. So keep in mind we've been looking only at glioblastoma stem cells. The final question here was let's move into primary data. This is the other real power of single cell RNA sequencing data. So it's just an absolute ton of public data out there. We're going to talk a lot about Crescent towards the end of the workshop, which is a data sharing program to put all this public data into one computable place. The take home from this slide is really the, the two, sort of the two ends of the gradient uncovered in glioblastoma stem cells are actually there to be found in primary patient tumors as well. They're just masked by this overall bulk population as well. And this is really where a single cell sequencing is so powerful because you can informatically pull out the suspected glioblastoma stem cells from these primaries and then look at these injury or developmental scores, specifically in those populations. And this is the model where this paper really landed on the idea that the glioblastoma stem cell populations within bulk tumors exist on this development to injury response access, and then these are essentially turning out the bulk tumor and they're actually on an alternate access around this astrocytic developmental program. And I did caution early on about the dangers of using these trajectory programs but they really are actually quite informative. As long as you're aware of some of the caveats and assumptions that the trajectory methods have coming in so specifically here, we have the glioblastoma stem cells existing on the developmental to injury response gradient, and then giving rise to these mature astrocyte bulk tumor cells. And single cell sequencing really allow me to score out and look at these signatures on a cluster by cluster basis. And that's really where this project is and where a lot of the thinking around single cell sequencing has come certainly a lot of genomics certainly when I was a trainee was really around binning so really putting cancer cancer types or cancer cancer specimens into sub types. Classical pro neuromus and chymol are sort of the bedrock of glioblastoma. The thinking is really starting to evolve with single cell sequencing as we see cells really being anchored especially by stem cell populations and really being a feature of development by and really not trying to necessarily place a cell into a specific bucket, but trying to understand where they may be on developmental trajectories, and potentially starting to map the position on these gradients to specific therapeutic interventions, especially in glioblastoma where really treatments have not changed and certainly survival has not improved very much at all over the last decade. And this is also really where a lot of the bioinformatic analysis stemming from single cell work land is really the need for secondary experiments so it's really important to think about how are you going to validate anything that you infer from your single cell data. Is it a functional screen? Is it a drug screen? Where we got this project is trying to look at drug predictions at the cluster level, but just like most genomic work, genomics in isolation really gives you leads or hypotheses to test but you never really have to plan out that additional validation experiment using orthogonal technologies like proteomics or functional genomics approaches. I just want to finish off with some of the gaps and opportunities certainly in research in cancer single cell genomics. A big one certainly is single cell methods aware of annual employability. So a lot of these methods, especially the batch effects, do not take underlying copy number variation into account. So some of these transcriptomes are extremely skewed due to little hyperamplicons or deletions of large chunks of DNA. The reference sets of single cell data from healthy tissue is extremely valuable, especially for answering questions around how normal cells inhabit or change or differentiate when they're within a tumor. And really where I certainly see the field is going is, what does the clinical single cell sequencing test going to look like? How do you interpret a profile? How do you make sense of multiple sub clones from inferred copy number variation? And how do you really roll these up into a distilled report that can be used to guide treatment patients? One step towards that is infrastructure. So you're going to get to learn how to use crescent today. This is essentially a cloud based RNA sequencing, a single cell RNA sequencing system. It takes raw data so allows you to keep your own data private but also to share it with your colleagues in a controlled way. So some of the heavy lifting is taken off of you. This is all done through a web browser and then some provided dashboards for quality control, working with clusters, labeling clusters, providing metadata. And then actually doing some analysis as well, the differential gene expression, and coming soon integration with multiple public data sets. So I encourage you to poke around the system. There's many cells from many data sets. Some of them are private, some of them are public. So welcome you to join the Crescent user group and continue to interact through the Toronto single cell expression working group. And this data that I just showed you're going to be working with is also available in Crescent so you can actually see that little GSC and bulk tumor pattern in a web browser. You can type in your favorite gene and explore to your heart's content. So this is now publicly released. So not enough time to workshop. This is sort of in the planning. Everything I've talked about is focused solely on the cancer cell population. Of course, there's a whole world of infiltrating cells that inhabit tumors. And this will exploring the tumor immune micro environment is likely a topic for another module. So I encourage you to sign up for that when it's fully developed. So there's the paper if anyone needs it. If you'd like to join the Toronto single cell working group, Toronto's in quotes because it's growing far beyond the bounds of Toronto, it's pan Canada, and actually quite actually several international participants as well. So that's the site there. Developers of Crescent listed there. And if you want to make data we offer this as a service through the Princess Margaret Genomic Center. One of the objectives of this handsome part of the of the session today is that by the end of this lecture you will understand the methods that are commonly used to analyze single cell RNA sick data. You will be able to select parameters for your analysis. So how to use state of the art tools to analyze your data. In particular, we're going to be using data sets from Leo Blastoma from a paper that was recently published by Trevor's group. I will give you the reference in the next slides. So the agenda for for this tutorial, Trevor already gave you an overview about where the single cell analysis field is going. I would say that this is a relatively new field, not more than seven years old, compared with other other fields in genomics, this is relatively new, and it has had very good number of tools been developed so we tried to select the best ones based on benchmark studies we will speak about it. And those are the ones that we will cover today.  Give you a broad overview about how a typical single cell analysis pipeline looks. And then we will enter into the methods themselves, we will start with Cell Ranger, which is a proprietary software developed by the company 10x, which is one of the technologies that Trevor mentioned in his talk, the Chrome technology. So it's essentially everything from taking the fast Q files that the sequencing facility provide you to map those reads against a reference transcript that you have to indicate it can be mouse human. And then we have single cell or single nuclei we will, we will talk about that later. The process that takes a while to run, it can take even in a high performance computer can take a few days. So we will not run this today but I will show you the results and how to interpret those results. In the modules that we have. So in the lab that we have for this module. So we will focus on this part here which is using a tool called Surat is developed by Raul Satija in New York and his group, and it does a second round of quality control check out after Cell Ranger.  A couple of other tools that I going to touch today are tools that we identified in a benchmark study as a top performer called ESPA. It's used for many other purposes like pathway enrichment analysis that I think you will see tomorrow and it's also using the bulk RNA seed field bulk RNA seed field. Essentially you want to rank the expression of genes in this case that are characterizing a certain cluster and you want to label those clusters based on those gene sets. And Trevor showed a heat map with the copy number variants for the glioblastoma data. And I will explain you how to use that tool and how to interpret the results. And finally, we will cover a tool that is being developed in in our lab with Trevor. This is called Crescent. It stands for cancer single cell cancer single cell expression toolkit. It's a web app is made user friendly is for people who don't necessarily have computational biology background. So you don't have to install any software dependencies. You just need your raw data and then you can run the analysis there. Again, the sample data that we will be using today is from glioblastoma from this paper. All right. So what's the motivation about doing single cell analysis in an automated automated way. We have biological technical and I will say bioinformatic motivations for this. The physical motivation is pretty much what Trevor covered in his in his talk. We have a lot of different cell types in the tumor micro environment, and we want to characterize those differences between cell types and all cell stages. In terms of technology, we have many different ways to measure single cell. Yeah, Trevor spoke about these two smart sick to antenna chromium, but there are some others, and they produce different types of readouts. The normalization has to be different. And, of course, you want to have tools that can handle different types of measurements, normalize them correct for batch effects, etc. You can have a better picture of the tumor micro environment with different with data from different labs or different studies. These two plots that I'm showing here are meant to show how the field has been growing in terms of data generation 2015 studies were published in about a few dozen cells per study. And then from 2015 it started to grow exponentially.  This is still growing exponentially. Not only the number of cells that are being measured is growing but also as a factor of the number of cells, we keep seeing more and more cell types and more cell clusters and assuming that the same cell clusters are representatives or of specific cell types or cell stages. 10 this is telling us that we still need to do way more sequencing to understand more complete picture about cancer and the tumor micro environment. Because we have all this wealth of information data from single cell we need powerful tools that can handle all this, all this data. The next number is I took it from this github repository where the community is curating computational tools that are being developed. So if you develop a new tool for single cell RNA sequencing you can go here and ask the coordinators to index it there so that the community have a control about who is doing what to avoid redundancy in terms of computational tools. This is I think for like at least one year old so it's probably around three or 400 computational tools that are out there now, just in this repository. In bioconductor this pass from two tools in 2016 to 15 2019 and there are about 100 these days just in bioconductor. So that tells you about how hot is this area nowadays but also for a computational biologist or data scientist in general. Sometimes having so many tools can be challenging because you don't know which tool you should use. If you see a nice paper they use the tool. You see another paper they use another tool and then becomes a little confusing which tool you should use. And I will tell you the approach that we're taking in crescent to address this wealth of data and computational tools being available. This is exactly what I'm trying to summarize in this slide. This is a typical single cell RNA sick analysis pipeline, it has nine steps, but they are not linear. Sometimes you do the QC and then you go to the clustering and then you go back to the QC I just listen listening listening the steps here for the purpose of illustration but sometimes is a process that goes back goes back and forth. I mean, if you're working with 10x data from chromium technologies, you will use cell reindeer. There is no need for using anything else because this software is free to use is very well maintained it has various releases during the year. And I have to say that they have a phenomenal technical support in the next. If you're working with 10x chromium technologies, you go straight to cell reindeer. The only thing that you have to take care of, in this case, is the reference transcriptome. If your samples come from human, come from mouse, you have to use the corresponding reference transcriptome or genome. And also you have to take care if it's in human, if it's the HG 19 version or GC 38, GRC 38, etc. So try to keep your things consistent. That's very important at the lab level, at least at all your students or your postdocs are hopefully using the same genome version, so that the data is comparable. The other thing that you have to take care when you're using cell reindeer is in the single cell field, sometimes in more on cancer samples or samples that come from a certain disease from a biopsy, etc. The samples cannot be processed right away in the lab. So sometimes they have to be frozen down and then stored in the cold temperature and then processed afterwards and one way to handle those types of samples is measuring the expression of genes within the nuclei rather than the whole cell. So we call that single nuclei instead of single cell, it will be SN, single nuclei RNA-seq. Because they come from the nuclei, the transcripts there don't have splicing yet. So the reference transcript that has to be used for cell ranger has to be indexed in that way, that still doesn't have splicing. That's one point that you have to take care when you run cell ranger. Now, in most of the cases, at least in the Princess Margaret hospital or UHN, cell ranger is run by the sequencing facility and the computational facility at UHN. So you will indicate in which reference transcriptome you want to use. Okay, so now once you have your REITS map to a given genome, you will have a matrix. Let me see if I have a slide here. Yeah, you will have a matrix like this one. It's called an empty X format. It has three files and is nothing else but one file that has the barcodes that identify each of the cells. So each of these DNA sequences is unique and represents one cell in the sample. And then you have a second file that is called features in all the versions of cell ranger. It was called genes.tsv. But the features are for single cell RNA-seq are genes. That's pretty much what happens is that there are technologies as was brought during the last part of Trevor's presentation. There are other types of measurements like methylation, etc., where the features might not be genes. They might be chromosomal regions. So the file is called features. And in the case of single cell RNA-seq, they are genes. So imagine that these barcodes are in a matrix, are your rows, or actually the other way. Your barcodes are the columns and the features or the genes are the rows. So you have a third file, which is called the proper empty X file, which has this format. You have the number of features, meaning genes, in that particular sample. The number of barcodes, meaning cells that were identified, and then the number of measurements, the number of time points in your matrix. And then you have three columns. You have the column indicating the feature index, which is the number 10 in this file. And then the number of the barcode index, which is the number one here. And so on. And then here you have the number of times, the number of UMIs, or number of reads, that this particular gene was measured in this particular cell. In this case, it was measured only once, sometimes it's twice, and so on. You can have hundreds depending on the cell and the gene. Why do we have this format? The main reason is because when you have big matrices, for example, in a single sample, you can have five to 10,000 cells, let's say 5,000 cells, and you have about 30,000 genes. So you will have a matrix of 30,000 rows by 5,000 columns. Now, think about the typical single cell RNA-seq study, you will have probably about 50 data sets, and then you will, sorry, 50 samples from multiple patients, multiple conditions. So when you integrate those matrices of thousands by thousands, you might end up having a matrix that is just not being able to be loaded, or it will take a lot of space. And the most important point that I haven't mentioned is that about 90% of that matrix will be empty, meaning will be zeros. Those are cases where a given gene was not detected at all in a given cell. So that's a waste of hard drive space. To avoid having zeros, the matrix NTX format will have only indices for cases where the gene was measured in the cell at least once. So those zeros are not included. Okay, so that's the output of cell range here. During today's labs, we will cover the rest of the steps here. I have to tell you that some of these steps are very memory intensive, and hopefully we can round them all. But I'm, I will to share with you the GitHub where I have all the documentation, and I also loaded into Senado, a repository, the intermediate files so that we can run them smoothly today's labs. Okay, so the second step is called is a quality control checkout cell range it has its own quality control that we are going to see in the next minutes. Actually, I should probably go over it now. Let me go back to my. This is another sample where you can see that that's low is not as pronounced as in the first one in the first one it was more like this, whereas here is more. The slope is smaller, right. That means that you have fewer cells with a high content of RNA, and probably some of these barcodes that are called cells, they were actually ambient RNA. You just have to be careful with that. And the next will actually give you some warnings in there in the output is telling us here that there was a low fraction of reads in cells, which is exactly what we're seeing here, we have. Ideally, they will ask you to have at least 70% of the reads to be within this region in blue. If he's less than 70% they will send you this warning. And I think if he's less than 30% or something this warning will be in red, and it will be an error rather than a warning. So this is kind of the first quality control checkout that you want to do as soon as you get your data back from the sequencing facility. Other types of warning that you might see are related, for example, to the number of reads. If this is too low, it might be that the indices, the sequencing facility where it didn't work out as well as expected, maybe there is a mismatch of the indices, etc. One common warning that you might have is a low fraction of reads mapped to the reference transcriptome or to the genome. So here, for example, we have about 95%. 95% of all the reads mapped to the genome, which is good. It's a very good number. If you have less than 70%, you will have a warning and I think less than 50% you will have an error. And that typically means that the reference transcriptome that was used for this analysis was seen correct. Perhaps you have a human sample, and the reference transcriptome was a mouse genome, or you use a single cell data and the reference transcriptome was single nuclear. So you have a lot of issues with the splicing there. Other things that you have to, of course, take care of is the type of chemistry because 10x has been released in different versions of the experimental kit. This is just something to have in mind. The newer versions of the chemistry have more genes and more reads per cell. So if you are using version one, it's okay that these numbers are in general lower than version two. And then finally, you also want to keep in mind which version of CellRanger you are using. I think there is version five now. But the main difference was between CellRanger version two and version three. Between those two versions, there was a big improvement in the algorithm that distinguishes what is background from what is cells. So if you have a sample that was mapped with CellRanger version two, I would strongly recommend that you run it again.  And actually, I will strongly recommend that whenever you are starting a new project, if you're getting samples from different labs, you're getting samples from publicly available repositories and then you want to add your own samples. If you have access to the FASTQ files, I strongly recommend that you run CellRanger from scratch. It's just computational time, but it will save you a lot of time afterwards in the analysis because at least in this case, you will have exactly the same genome annotations. You will have the exact same number of genes being measured, etc. I will talk a little bit later about, it's not easy to get FASTQ files always from publicly available or data that is supposed to be publicly available. Sometimes it's not that public, to be honest, but if you have access to the FASTQ files, I will recommend that you always run CellRanger. Okay, let me go back now to the slides. Do we have any questions so far? If not, we can come back later always. All right, so we are in step one, step two. These steps will go, I will go faster because we will see them live with the labs. So the step two is QCE, here we want to see if there are many transcripts belonging to mitochondrial genes, which is usually an indicator that when the sample was processed in the lab, there were many cells that were dying. When the cells are dying, often they start to express a lot of mitochondrial genes just as a general stress response. So if you have a lot of mitochondrial genes in your cells or in your sample, chances that that sample was, the cells in that sample were dying are higher. So maybe you don't want to consider that sample because then you are not seeing the biology of interest, you are just seeing a general stress response. You don't want that. Then once you have these two quality control steps, you do normalization of the samples, I will tell you more about it, essentially, to factor for- Sorry, I have a quick question. So cells dying, is that a more common thing in cancer cells or just aren't a single cell? I think it's just common with the way the samples sometimes are processed. I wouldn't be able to tell you if it's more common in cancer than in other types of tissues or in other types of samples. But certainly the way some cancer samples are processed are more prone to damage the cells. I know, for example, liver tissue from cancer, the liver is very, very, how to say, is very prone to the cells to start dying. So that can become damaged very easily. Yeah, I think one nasty artifact is ambient RNA from necrotic tissues, where you essentially start floating around and they get encapsulated in the droplet on a 10x platform. Sure, sure. I wouldn't say it's necessarily higher than cancer versus others, but definitely highly necrotic tissues. You could expect a higher ambient RNA level. But yeah, I don't think anyone's really done like the systematic cancer versus non-cancer comparison. Okay, great. Thanks guys. No worries. The good thing with single cell technologies is that computationally, you can try to identify groups of cells that presumably were dying and separating from the rest. I mean, compared with bulk RNA-seq, for example, where you have everything in a mixture and you cannot decompose with that very easily. We can talk about that during the tutorial. Then you do normalization. Here we are essentially trying to correct for sequencing depth. Some samples might have been sequenced deeper than others and we want to control for that. If we do something called batch effect correction, we will spend most of the time during the tutorial going over batch effect correction. And this is, for example, when two samples come from different labs, they were prepared by different people or even using different technologies, 10x, Chromium versus Seqwell, etc. This is correct for technical artifacts that will otherwise mislead the biological assumptions that we are making. The fifth step, once we correct for those batch effects, we integrate all the samples into one big matrix and then we do, because we will have a very high dimensional matrix of thousands of genes by hundreds of thousands of cells. The analysis with that big matrix will take forever. So we need to reduce the dimension and typically here we use something like PCA or RPCA, we will see it in the tutorial. Then with the dimensions being reduced, we cluster the cells just with the features, meaning the genes, that were more informative. Another example by that is we want to keep the genes that were well measured in many cells, right? We want to remove genes that are poorly measured in most of the cells. But also we want, know that they are just housekeeping genes, that they are expressed always in all the cells, because that also don't tell us any information. That are well measured, but at the same time they are having changes in the samples and in the cells. So with those genes, we will do the cell clustering by comparing the gene expression profile of the cells with this dimension reduction. And then here we do differential gene expression. We want to see if group of cells is expressing what genes are different, differential expressed in another group of cells, etc. And then as a data scientist, you want to show these to your audience, which can be your paper readers, can be your collaborators. So you want to put all those tables and those analysis in a nice way, using visualization tools like the UMAP plot, heat maps, etc. that we're going to see today. So cell cluster labeling. So instead of telling your collaborators, I have 10 clusters, you can tell I have five cell types, which are split into 10 clusters, something like that. So in Crescent, just quickly, the way we are selecting some of these algorithms or methods to do each of these steps is that we rely on benchmark studies, either done by others or by ourselves. And we pick the top performers, and that's what we implemented in Crescent. Okay. This is the data that Trevor was showing in his talk from Laura and others at Trevor's lab. And in particular, in the tutorial today we are going to focus on these seven samples, or are from glioblastoma, but you can see that they have different number of cells, they have different types of cells. For example, these are all malignant cells from all these seven samples. So I thought it was a very nice example to work with today. Hopefully we can run all seven. Actually, I know that we will remove at least one because of computational limitations in some of the steps, but hopefully we can work with the remaining six. We will just work with three of them or so. And then once you have time and more computational power, you can essentially copy the same code and run all seven or more or more samples from this paper. We have all the data from this paper in Crescent, and I will tell you how to get it.


Comparator and parity checker. At the end of this session, student will be able to design ALU Comparator and Parity Checker. ALU provides fast arithmetic and logic functions. It handles larger words and it is a key part of many CPUs. This is one of the ALU unit 74181. So, here you can see A0 to A3. This is first input B0 to B3. This is one more input and this is optional input that is carry input. If we want to carry the last carry then we can make it on. Then we are having select input S0 to S3 means 4 bit inputs are there and it will be create 16 inputs. Then mode M it will be having different modes. Then this is the output and this is again supplementary outputs are there. We will see one by one how they are creating ALU unit. Now, as we have seen that selection modes are there S3, S2, S1, S0. So, we are having this 16 inputs. Then mode this is the mode control. So, when mode is high we are getting output logic functions and mode is low we are getting arithmetic function. Again in the arithmetic we are having with carry or without carry. So, we will see one by one. So, for this selection mode we will select it first A0 to A3 and B0 to B3. The inputs will be there for that when all S3, S2, S1, S0 all are 0 that time whatever the A input is given the invert it is inverting. So, this is the logic function for the inverter input A. Then next is there it is giving A plus B bar. So, this is the NOR function. Then next is there low, low, high, low at that time you are getting A bar B. So, these are different logic functions we are able to calculate with the help of this selection and this 7, 4, 1, 8, 1 ALU unit. Now for arithmetic functions as I said it is with carry or without carry. So, when CN is high there is no carry and CN is low with carry. What is the requirement of this carry is when we are having only nibble functions inputs are there. So, when we have to add with carry so it is automatically going for plus 1. So, arithmetic functions when all lows are there so it is as it is input is showing without carry but with carry it is showing that A plus 1. Now one more we will see that when it is S3 is low and other is high that time it is showing that functionality A B bar minus 1. And here it is showing with carry so it is A B bar. So, here we can calculate 2's complement also with the help of this combination low, low, high, high. So, it is having a minus 1 so it is a giving us 2's complement. So, there are 16 combination for the selection so that we can calculate 16 logic function 16 without carry and 16 with carry. So, total 48 arithmetic logic functions are there. So, for example, we are having here addition of A and B. A is 97 and B is 29 so these are 8 bits. But in this case we are having only a single nibble addition so at a time we can add 4 bits. So, here we are setting when lower bit addition we are doing we are putting mode is equal to 0. So, it will go to the arithmetic then S is equal to 1 0 0 1. So, this is nothing but addition in this operation we can see that A plus B is there. Then for this lower bit we put C n bar is equal to 0 here it is showing everything over here in this table. So, this is the A it is having mode 0 for arithmetic 1 0 0 1 it is for this addition selection. Then this is the lower nibble of A lower nibble of B and carry is put as 1 means it is with the no carry. So, it is giving output here. So, like this we are again doing the most significant. So, A B here we are considering carry and we are doing this total addition. So, next is example is of on the same numbers we are doing the minus. So, 0 1 1 0 that is the selection mode we are putting that then A and B and without carry we are doing this operation for the minus. And this is a one more function we are showing here example that is B it is again doing that plus and then here minus. So, there is one more function with the IC that is the comparator. So, here comparator is shown 7 4 8 5. So, here again we are using A 0 A 1 A 2 and A 3. So, total 4 bit numbers are there A 1 and B is 4 bit numbers. So, what is the function of this digital comparator digital comparator is comparing whatever the inputs are given. So, here we can see there are three different outputs it is showing A is greater than B A is equal to B or it is A is less than B. So, between any two numbers it is showing the comparison and as per that it is giving the output. So, if it is true then it is giving output as 1. So, here pin diagram is shown from 1 to 16. So, every pins number and its functionality is given. So, if it is A is greater than B A is equal to B like this and this is the truth table of this digital comparator. So, here we can see 1 or 2 example here 0 0 and B is at that time 0 0 means it is A is equal to B. So, here this A is equal to B is 1. Next if we see this example A is 1 0 and B is 0 means A is greater. So, here A is greater. So, these are output of the digital comparator. So, when our number is more than 4 bit that time we can cascade this digital comparator. So, this is the comparator 7485 here you can see that 7485 1 2 3 4 5. So, how your number is there or your requirement is there you are putting the digital comparator in the cascading and then total again you are connecting one more comparator here and the output will be this cascading output of this all this number. Now, we will see one more circuit or functionality that is a parity checker that is 74180. So, what is a parity checker? First we will see that parity checker is nothing but it is a circuit that checks the parity. So, it is called as a parity checker. So, when even parity is there means number of one in any number are in even numbers then it is called as a even parity. And when any number is there and it contains odd number of ones in that number then it is called as a odd parity. So, this parity checker 74180 will show that if your number is odd parity or even parity. So, the truth table is given over here. So, accordingly you are having the number that any number here 8 bit number is there. So, a b c d e f g that is any number it is providing to this and then if we are going to check if parity is even or odd. So, accordingly it is giving the output of that number and this two more pin are there for having cascading. If we want to cascade that parity checker I see then we can cascade with the help of this numbers. So, one question is there what is the use of above circuit? Here I am showing circuit is 74180. So, what is the use of this circuit? So, this circuit is nothing but parity checker as just explained about this parity checker. So, whatever the number given to you with the help of this IC it will provide you output if that number is having either even parity or odd parity.


Next one is called the in-deep game which is connecting power so it means to connect all your attacks together in a chain So let's say this happens. This happens Okay, so you connect in a chain and you don't stop right because it's not like one two three and then you back up It's like connecting your attacks together one two three four five six seven eight nine ten eleven twelve Connect them all together


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Welcome to the AI for Good Global Summit. My guest is Maya Matarik. Good afternoon. Hello. Maya is a professor in computer science at the University of Southern California. Maya, thanks for joining us. It's my pleasure. Tell us, first of all, what did you work all about? Well, I'm trying to do AI for Good. And in particular, I'm trying to do personalized robotic companions for Good, which may seem like a pie-in-the-sky prospect. But in fact, for the last 20 years, we and others have been developing a field called socially-assisted robotics. And the idea is that we can create very inexpensive, these days, almost only $300 US, little robots that help you with whatever your specific challenge is. So your challenge might be that you have a child on the autism spectrum and they need help developing social skills. Or you might be a survivor of a stroke and you need to relearn how to use your arm. Or you might be an elderly person who has Alzheimer's disease and is really losing contact with the world and you just need something to keep you going. So no matter what the individual challenge may be, we're interested in developing these personalized, socially-supportive agents that are really actual physical robots. So that's what we do. OK, interesting. But what sets you aside from your competitors? Well, there are a lot of technologies available today in the form of apps or digital interfaces. But unfortunately, for those technologies, we as humans are wired to be very social in this physical world. And so we are most motivated and rewarded when we interact with other humans. So as much as most of communications today is done through, let's say, messaging, unfortunately, that's actually making us more sad and more depressed. What makes us generally more happy is face-to-face interactions with other humans. But sometimes you don't have that other human there because they're working or sick or unavailable. Or in many cases, people are lonely. And so instead, when that human is not available, that's when we bring in that socially-assisted robot. And you've spent a couple of days here at the summit. How important is it to have a platform like this so that people can talk about AI and the advancements in it? I think it's incredibly important to talk about what's happening in AI right now because this is an amazing, pivotal existential time. And I don't say that easily. I've been in the field for about 30 years now. And I've never thought that. I've never believed it. People have often said, oh, this is it. And I never thought it was. And it wasn't. But this time it really is because we have a technology that is immensely powerful. We don't really understand it. And it has major implications on the future of work, the future of well-being. And so we really need to take it seriously. And you talk about that. And in fact, AI has gathered such a pace in particular in the last year. Where do you see it going in the mid to long term? I'm concerned about the pace of AI development. It is already happening. So it's not a matter of stopping it or slowing it down. But it is a matter of understanding that we have developed something that we don't know how to control and we don't fully understand. And that's the first time ever that this has happened, that level of complexity of a machine. And so what we need to do is we need to really get technologists, the people who are developing this, along with policymakers, along with the general public. And we really need to do regulations sooner rather than later. And then we need to adapt and refine that regulation. I'm actually very concerned about what's happening in the United States, where I come from, where we have the home of most of the companies that are generating these amazing technologies. And the technologies are amazing. And they do have amazing potential for good. But they also have tremendous potential for not so good. And because the companies are not incentivized to regulate and because no one really has figured out how to regulate it, we are very slow in the United States on this front. And we need to move faster. We can make mistakes and fix them along the way. But if we do nothing, that is the biggest mistake. What are your thoughts about the guidelines that you've just spoken about? Obviously, more needs to be done, as we've been hearing from lots of people at the summit. Well, so beyond, of course, we should sit and talk. So beyond just talking, I'm all about doing. This is why I create physical machines. We have to act in this world. And so I think, first and foremost, we should not be releasing things out there to the general public at this point anymore until we understand better how they work. Because we have had too many examples of strange, unexpected behaviors. So that's one problem. And even in some of my work that I'm doing and collaborating with Google, we're working very hard to understand what these behaviors might be in order to corral them. But we should not go and make things more powerful in the public. I was listening to Harare just yesterday at the summit here saying something that I completely agree with, which is there's a difference between development and deployment. It's hard to stop development, but that's different from deployment out in the world in the open when there is no control. So that's one thing that needs to happen. The other thing that needs to happen is much more understanding of what is being publicly deployed. If you're going to let something out, you need to understand how it works. And if you don't understand how it works, you need to pull it back. So for example, something that is quite scary to contemplate, in the United States, we have already had an example of a large language model being used, like chat GPT, to basically put an advertisement on the web to hire people. And those people were hired to do something in the real world without knowing that they were hired by an AI. That's really concerning. So we should worry about these implications. Also, I will add that we should worry, also in the long run, about implications on both democracy and truth and knowing what information is real and what information is fake. And also, implications on work. I'm very concerned about the creative arts. I'm concerned about music, film, digital arts. In what way? Well, I'm concerned because the world of artists has always been difficult. It's getting jobs, getting a gig is very difficult. And if so much more work can be done by AI, then quite well. We can argue that it's maybe derivative, but so much human work is available for AI to be trained on that what AI creates is quite impressive. And it's cheap. And so if we're just hiring that, what happens to human artists? And we could say this will help human artists, but nonetheless, economies work the way they work. The cheaper, good enough art will be bought and used before more expensive human art. So there is a real concern, I think, that we should think about there. Maya Matarik, thank you so much for your insights. Thank you for your time. Thank you. And more to come from the AI for Good Global Summit here in Geneva.


My name is Lieutenant Colonel Jennifer Obanek. I've been in the Guard for 22 years, and I'm an A-10 pilot. My dad flew A-7s and F-16s, and that's when I started in my love of aviation, when I was very, very young. I started flying so young when I was two days old, and I flew with my dad, he owned an airplane. And I flew with him all the time, and I just was like, this is what I want to do. And I always wanted to fly the A-10 as well. It was my dream airplane and my dream location. And back then, I mean, women weren't allowed to fly fighters, so I was 16. I was a junior in high school, and I remember that it was really big news that women are allowed to fly in combat finally. I remember that watching it on TV and Les Aspen was the Secretary of Defense, and he made the big announcement, and it changed my life. First of all, the services are to allow women to compete for assignments in combat aircraft. Women pilots in the military are about 20%. Fighters, it's one-tenth of one percent are women. I was the 11th female to fly the A-10. So you're more likely to be a left-handed Major League Baseball pitcher than to be a female fighter pilot percentage-wise. At first, it was not with really welcoming arms that women were brought in to fly. And then there was growing pains. I had to be 25% better than the men in order to get the same recognition to the same level. I really wasn't accepted until after I dropped moms in combat and did my job there. And after that, it changed. And then it started incrementally changing for the rest of my career. Nowadays, it's much more accepted. If you can see her, you can be here. Four or five years ago, we got some younger pilots, lieutenants, and I remember telling them a story one day over lunch about some things that had happened back in the day. And they were just absolutely shocked that I would get treated like that because to them, it wasn't weird. It was normal that women could do anything they wanted to do. Most people, as long as you can do the job, legitimately don't care. I'll tell you the guys I helped in Afghanistan to interact and care. I think the world nowadays, especially, is so willing to give anyone a chance that it's an amazing time to be a woman or a minority. And once you tell yourself you're amazing, and once you tell yourself you're amazing enough times, you'll start to believe it.


Very important question I mean during the last two weeks I mean we've been discussing many important issues and matters were finalized so on the admin side we had the four year rolling plan of the rolling operational time of the at the Union 2022 to 2025. We also approved the establishment of a council working group for the elaboration on draft strategy plan. We have also the endorsement of the ITU policy for persons with disabilities and special needs. There was a lot of concentration on greening of the conferences of the of the ITU virtually conducted no travel, no carbon, no budgets spent on this. The savings from such events conducting events virtually were discussed also and the council member states authorize the secretary general to use the excess budget saved from conducting the events virtually to perhaps utilize it in the future for fellowships for trainings and for other matters that are that will benefit everybody. It's many actually it will be unfair to give one but it is the understanding that the year this year and last year it was an exceptional year for everybody. Everybody's support helped continue the work of the ITU. ICT being the core actually in dealing with this pandemic and continuing the business of everybody and the ITU being at the helm of that requires support from all the member states and it is the understanding is that recovery cannot happen overnight and that we have to think and plan for it to be taken gradually with caution so that we don't burden ourselves and we don't burden the efforts and budgets and everybody. So this is the main event plus the concentration of, as I mentioned earlier, I mean persons with disabilities and special needs. I mean endorsement of a policy within the ITU strengthens and shows that the importance of inclusiveness of all gender balance and such matters are great to be discussed and approved within this meeting. A great question. I mean everybody's heard about I mean affected by this pandemic. We have had two virtual consultations in the last year. This edition of the virtual consultation is different. The length is more. We are more cautious about planning the day-to-day meeting. And achieving the expected results and as far as today is concerned, I mean we have been doing great with the support of the member states, the councillors and the well-preparation by the secretariat. So all of that is through evidence that this year we have dedicated nine days for this virtual consultation so that we can move ahead and learn from the lessons of the previous sessions of this virtual consultation. We are bound by the rules and regulations. I mean we have procedures and rules governing these meetings within the boundaries of the legal matters. I mean all member states agree to move forward and help continue the business of the council and the union. I would say continuation of the pandemic would be a challenge. The new headquarters building is a challenge, especially in those in these circumstances. Having rules of procedure that are designed, that may not have been designed to cater for these situations is a challenge, but the opportunities are many also. The lessons learned from are all opportunities. I'm sure that the future events of the ITU, the three major events and beyond, I mean will have many changes to address this. ICT is a key in business continuity and I believe the ITU has to demonstrate how ICTs can help in emergency situations and this is vital for business continuity. What I would like to add is that this is not the first pandemic nor it will be the last and recovery cannot be expected to happen overnight. So we need to work together in different ways and learn to always move forward. So with that I think I can thank you for having me. I look forward to seeing you in person and tell them be safe and healthy and goodbye.


The workshop is essentially about exchange of information between Africa and Asia in terms of conservation and management of Great Apes and it was kind of born out of a workshop that we had in Missindi in Uganda last year, organised by International Institute of Environmental and Development, who were based in London. And I was invited to go along and give a sort of general overview of the orangutan situation in Asia, just from a different perspective. And one thing that became clear from the workshop was that there are incredible synergies, but also differences between the way Great Apes are managed and conserved on both continents. And what we thought was we bring together African practitioners and policy makers to Asia and with their counterparts from Asia, sit down and we discuss issues related to lessons learned, if you like, from best practices in Africa, for example, eco-tourism is much more developed in Africa and compared to the situation in Asia where the orangutan conflict, if you like, is somewhat sensitive, but also very pertinent and contemporary at this moment in time. What we've come up with is a series of policy recommendations that are applicable for both continents and we'll be developing a policy brief and disseminating that as part of a concrete output from the workshop. The other thing we want to produce is a series of best practice guidelines linking Great Ape conservation to poverty alleviation. Most Great Ape ranges occur in areas where people are very poor. The pressures on the populations and the habitat that they're in are often generated by poverty, but also linking the poverty angle with development issues. So here in Indonesia, one of the major drivers of orangutan threat, if you like, is expansion of oil palm, for example, and clearing orangutan habitat. In Africa, the pressures are more focused on small-scale farmer exploitation of forest and conversion. And so it's sort of marrying these two issues, if you like, and creating some kind of policy environment. And so we'll be producing a sort of series of best practice guidelines of how you integrate local livelihoods into Great Ape conservation. And here we are in the Nehru-Menteng area where there are a number of islands where orangutans are being rehabilitated with a view to them being released into the wild. And so that's, again, a very interesting perspective from the African situation where Great Ape tourism is very well developed, and it's something that we're looking into promoting here much more as well. So the three main subjects we've been looking at in terms of the policy implications of Great Ape conservation in Africa and Asia are the possibility of Red Plus co-benefits contributing towards conservation of Great Apes, issues of human wildlife conflict, and thirdly, rather, ecotourism. So we've developed a series of policy recommendations based around those three themes. The first Red Plus co-benefits include biodiversity conservation, so that could include protection of Great Ape habitat. So by default, if you like, Great Apes could be conserved through long-term payments to preserve carbon in high biodiversity valley forests. In terms of ecotourism, the sector is much more well developed in Africa. For example, Rwanda and Uganda obtained a large contribution of their GDP comes from Great Ape tourism. In Rwanda, I think it's the third most important income generator and access to foreign exchange. And in terms of human wildlife conflict, we're looking at ways to mitigate the ways in which humans and Great Apes come together and basically are forced into conflict situations. And so those three pillars, if you like, will form the basis of these series of policy recommendations. Ecotourism is very interesting because in Africa, the ecotourism sector is very exclusive. It's high value. You pay $500 for a license. It's very well controlled. The guidelines are extremely strict. You're only allowed to view the apes for up to an hour. It's very, very closely controlled, but because of that exclusivity, people are willing to pay a high premium. In Asia, it's much more part of the mass tourism industry, so people come to Indonesia and they take a series of boxes.  And so there's less emphasis, if you like, on the exclusivity. And I think, unfortunately, there are some fabulous places to see orangutans. Tangin Puting, for example, here in the Aramenteng, you're guaranteed of seeing them. And it's actually very cheap to do so. And I think there's a greater willingness to pay on behalf of the tourists than that is actually being exploited. So there's a lot of potential in the ecotourism sector in Indonesia and Malaysia, for example, to increase revenues and place more value, if you like, on the ecotourism sector. Bringing together practitioners, I think, has facilitated that process, has been the most valuable. And you can see behind the camera, there are guys who practice orangutan conservation in Asia, exchanging experiences with guys who are doing the same in Africa. And that's the real value of these kind of workshops and seeing things in the field, as we're doing now. In terms of future activities, and we're hoping to produce these best practice guidelines, series of best practice guidelines for great ape conservation and poverty alleviation. And maybe that can be expanded to a wider remit. And one thing I'm particularly interested in, this is issue of Red Plus and how high biodiversity forests, which include great apes, can be included under the red mechanism. And perhaps as we're discussing future plans, that may be the emphasis of the next workshop between the organised between IRID and C4.